Forward Deployed Machine Learning Engineer
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
Engineering
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
About the Role
We're hiring a Forward Deployed Machine Learning Engineer in our Benchmarks and Evaluations vertical. You'll be the first MLE dedicated to this vertical and will work directly with the GM and our researchers to scale Protege’s position as a renowned leader in the space.
At Protege, we believe that real world data is one of the largest bottlenecks to AI progress. Our data and data expertise position us to be neutral arbiters for the market, helping model builders understand the current performance of their models, identify what data will improve performance, and show that improvement over time. Benchmarks and evaluations power that cycle. As an early engineer in the Benchmarks and Evaluations vertical, this role is an opportunity to help build the technical foundation for a critical area that greatly benefits current and future customers.
What You'll Do
Work on the eval foundation
• Partner with the GM and early customers to define what constitutes strong evals in different domains
• Work with Protege researchers to design and build benchmarks
• Build the standards on how different modalities should be processed
Own infrastructure
• Build the backend the vertical runs on which includes data pipelines, execution environments, storage, and orchestration
• Stand up sandboxed environments for agentic evals, where models need tools, code execution, or multi-step tasks
Go from fast iteration to product
• Find repeatable eval patterns, infrastructure gaps, and product opportunities from live engagements
• Partner with DataLab (our research team) on domain-specific data and research questions
What Success Looks Like
In the first 90 days, we expect the following:
• Build an understanding of the evals landscape, the GM's strategy, and customer demand
• Build an understanding of what our platform and data partners can support today, and where the gap is for eval building
• Identify the largest technical bets and ship multiple iterations of the eval infrastructure
• Own the engineering portion of customer engagements end to end
What You Bring
Must Haves
• 4+ years of engineering experience
• Hands-on ML work evaluating models
• Have previously owned backend and infrastructure
• High ambiguity tolerance and bias to action
• Comfort working with urgency to meet the pace and volume of the market demands
• Strong written communication
Nice to Haves
• Prior experience building benchmarks, evals, or human data pipelines for LLMs
• Time at a frontier lab, an eval-focused team, or a research org
• Founding or early engineer experience at a fast-moving startup
• Familiarity with agentic systems, RL environments, code-execution sandboxes, TEE/TREs
Protege's Values
Pass the Loved Ones' Test
We act with integrity and do the right thing - especially when it's hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
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Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
About the Role
Protege is hiring a Senior Software Engineer for our Presentation team, the squad that builds the surfaces where Protege's data catalog meets the people who use it. That means two products: the internal dashboard our team uses to catalog, curate, package datasets, and the customer portal where AI labs browse, stream, and evaluate content before they license it.
This is a product engineering role in a TypeScript and Next.js codebase. You'll talk directly with the people who use what you build, design the interfaces yourself, and ship the whole feature: UI, API, data model, and tests. The datasets behind these screens are large and multimodal (video, audio, motion capture, documents, medical imaging), so a lot of the craft is making that volume feel fast, legible, and good to use in a browser.
As a senior engineer, you'll take on the largest and most ambiguous initiatives on the team: the ones that span both apps, cross into other squads' services, or need someone to figure out what the right product even is before building it. You'll partner with the team lead on direction and priorities, lead design on complex workflows, and raise the bar for everyone shipping in this codebase. This role is ideal for product engineers who want to design what they build, talk to the people who use it, and own features end to end.
What You’ll Do:
• Lead the team's larger initiatives from discovery through design, build, and rollout, such as search previews at catalog scale or the presentation of a new content modality
• Talk directly with the partnerships, solutions, and sales teams who use what you build; understand the business context behind each request and bring that view back to the team's priorities
• Design complex workflows and data-heavy interfaces yourself: information hierarchy, interaction patterns, and every state a screen can be in
• Build the workflows our partnerships and solutions teams use every day: sample creation, customer access management, and delivery exports
• Build the customer-facing experience for browsing, streaming, and evaluating multimodal samples
• Make large multimodal datasets feel instant in a browser: streaming playback, previews, lazy loading, and export at scale
• Build UI on top of embedding-based search so users can find the right clips within catalogs of millions
• Decide what runs client-side, in a route handler, or in a backend service, and work with our data teams when the answer crosses squad boundaries
• Handle sensitive data correctly: scoped access, secure share links, and PHI-aware routes
• Set the bar for quality on the squad: testing, performance, design consistency, and UX detail
• Lead design discussions, review code, and unblock other engineers
• Turn repeated one-off UI and workflow patterns into reusable components in our shared design system
What Success Looks Like:
30 days: Learn and Build Relationships
• Get productive in the codebase and ship your first improvements to both apps
• Build a working map of the presentation stack: the two apps, the services behind them, and how content flows from ingestion to a customer's screen
• Meet the partnerships, solutions, and data teams; understand how the catalog gets licensed and delivered, and where the current tooling slows a deal down
60 days: Develop and Cultivate
• Take a major feature or workflow from ambiguous ask to shipped, including its design
• Start raising the bar on quality, design consistency, and performance across the squad's code
• Become the engineer others come to when they're stuck in the presentation layer
90 days: Make an Impact
• Lead a significant initiative end to end, from talking to the people who need it through design, build, and rollout
• Identify at least one leverage opportunity (a reusable component, an architectural improvement, a workflow that should be self-serve) and drive it
• Have a visible effect on how the team ships: patterns adopted, reviews that teach, fewer things falling through cracks
What You Bring:
Must Haves
• 5+ years building production web applications that real users depend on
• Deep TypeScript and React experience, with Next.js or a comparable full-stack framework in production
• You've shipped user-facing features end to end: UI, API, data model, and tests
• Strong design judgment: you can design a complex workflow yourself, hold a bar for visual and interaction detail, and articulate why one UI decision beats another
• Solid backend fundamentals: API design, Postgres, auth and permission modeling, and cloud services (we use AWS)
• You've built data-heavy or media-heavy interfaces and know how to keep them fast: streaming, pagination, caching, and knowing what not to load
• You've worked directly with the users of what you build and let that shape what you shipped
• Attention to detail without losing speed, and a bias to action
• Curious and proactive
Nice to Haves
• Experience with media playback in the browser (HTML5 video/audio, HLS, containers and codecs)
• Experience building UX over embeddings or vector search
• A portfolio, side projects, or shipped work that shows your design taste
• Experience building or maintaining a design system across multiple apps
• Familiarity with auth systems (we use Clerk) and permission modeling
• Experience working with sensitive or regulated data (HIPAA, PHI)
• Prior startup experience as a founding or early engineer
Protege Values
Pass the Loved Ones’ Test
We act with integrity and do the right thing — especially when it’s hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
Senior Software Engineer, Data Processing
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
Engineering
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
About the Role
Protege is hiring a Senior Software Engineer to own the data processing layer at ingestion — the part of the platform that takes large-scale source data and turns it into clean, structured, enriched, validated, AI-ready datasets. This is a hands-on, backend- and data-heavy role with end-to-end ownership of the pipelines that move and process data at volume.
Protege connects organizations that hold high-value data with the AI builders who need it. The value of that exchange depends on what happens at ingestion: raw, varied, high-volume source data has to be processed reliably, securely, and at scale before it's useful to anyone.
You'll work across imaging, audio, video, and other data modalities, crossing healthcare, media, and other disparate industries and data partners. You’ll partner closely with product, Data Lab, and partner engineering teams to build robust ingestion and processing systems for structured and unstructured data at massive scale, from millions to billions of records, files, and other source objects. This role is ideal for engineers who are energized by messy data at scale, want deep ownership of critical infrastructure, and like turning ambiguity into reliable systems.
What You'll Do
Ingestion & Processing Systems
Design, build, and operate the ingestion systems that process large volumes of multimodal data into usable, well-structured datasets
Own the ingestion path end to end, from how data lands to how it is validated, processed, tracked, and made available downstream
Build modality-specific processing steps for real-world source data, such as medical imaging processing, audio and video metadata extraction, quality validation, and notes processing
Build parsers, validators, and normalization logic that can systematically handle messy, non-standard, and high-variance source formats
Turn repeated one-off data handling work into reusable processing patterns, internal tooling, and platform capabilities
Scale, Performance & Reliability
Build for high volume and high throughput, optimizing systems for reliability, cost, and speed
Work across distributed and parallel compute systems to process workloads that do not fit well on a single machine
Choose the right execution model for the workload, including batch processing, distributed execution, and modern compute patterns for unstructured data and inference-heavy processing
Diagnose and resolve bottlenecks across ingestion and processing systems, and keep performance from degrading as volume and modality complexity grow
Data Quality, Security & Compliance
Build validation and quality checks that catch bad, incomplete, or malformed data before it propagates downstream
Handle sensitive and regulated data, including PHI, with the security and care the domain demands, including de-identification where required
Track provenance, metadata, and usage constraints through the ingestion path so downstream use remains compliant and auditable
Raise the quality bar for observability, debuggability, and operational reliability across the ingestion layer
Cross-Functional Partnership
Partner with product and Data Lab to support new modalities, new partner requirements, and non-standard source data
Work directly with partner engineering teams when needed to translate source-system realities into robust ingestion and processing design
Surface recurring patterns that are worth standardizing into reusable transforms, validators, and internal tooling
Help shape how Protege handles new data types as the platform expands into more complex data environments
What Success Looks Like
30 days: Ramp
Get productive in the codebase and ship your first improvements to existing pipelines
Build a working map of the ingestion and processing stack, the major data flows, and how we handle each modality
Meet the engineering, product, and Data Lab teams to understand how the function operates across the company
60 days: Take Ownership
Own a processing pipeline or modality end to end, from ingestion through delivery of AI-ready output
Develop depth in how we handle one or two data types at scale
Start raising the bar on data quality, observability, and processing best practices
90 days: Operate Independently
Own a significant part of the ingestion and processing layer and lead design on new modalities or scaling challenges
Ship reliably with minimal hand-holding, and help unblock others working in the data layer
Identify at least one leverage opportunity — a reusable transform, tool, or architectural improvement — worth investing in, and drive it
What You Bring
Must Haves
5+ years building and operating production backend or data systems, with real experience in data processing at scale
Hands-on experience designing and running large-scale data pipelines
Strong programming skills in Python
Experience with distributed data processing
Strong proficiency with AWS
Comfort with messy, varied, high-volume data and high ambiguity, with a knack for finding patterns in complex environments
Attention to detail without losing speed, and a bias to action
Excited to work on a product built around moving and processing large volumes of data
Curious, tenacious, and proactive
Nice to Haves
Experience processing one or more specific modalities at scale: medical imaging (e.g., DICOM), text, audio or video
Background working with sensitive or regulated data environments (HIPAA, healthcare compliance, PHI handling)
Experience with streaming systems or workflow orchestration (e.g., Airflow, Dagster)
Experience with GCP and Azure
Prior startup experience as a founding or early engineer
Familiarity with ML, NLP, or LLM-based systems, including embeddings and fine-tuning
Protege Values
Pass the Loved Ones’ Test
We act with integrity and do the right thing — especially when it’s hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
Role Overview
We’re hiring a founding Forward Deployed Engineer to help build a new vertical from the ground up.
You’ll be the first FDE dedicated to this vertical, working directly with the GM to define the market, strategy, and early commercial motion. Your job is to turn early customer demand into durable technical capability: defining what the vertical needs, building reusable infrastructure on top of our existing platform, and establishing the technical patterns that future engagements and future hires can build on.
This is not a standard implementation role. It sits at the intersection of engineering, product judgment, and customer reality. You should be excited to work from first principles, operate in ambiguity, iterate quickly, and partner with product engineering to make strong calls about what should become a core platform capability versus what should remain vertical-specific.
What You'll Do
Build the Technical Foundation
Partner with the GM and early customers to define what the vertical actually needs technically.
Build the first MVP of reusable patterns, integrations, and tooling that future engagements will run on, leveraging our core platform where it fits and extending it where the vertical requires something new.
Make architectural decisions about what belongs in the platform layer versus vertical-specific tooling.
Create the initial technical playbook so future FDEs can build on a real foundation rather than starting from scratch.
Own First Deals End-to-End
Lead the first customer engagements in the vertical, from technical scoping through delivery and post-launch support.
Write robust code that solves immediate customer problems while compounding into reusable infrastructure.
Navigate real-world complexity across customer data, integrations, workflows, and stakeholder dynamics.
Translate messy customer requirements into systems that are durable and maintainable.
Shape What Becomes Product
Partner with Product and Engineering to identify which patterns from early customer work should become core platform capabilities.
Surface repeatable use cases, infrastructure gaps, and product opportunities from live engagements.
Help determine when the vertical is ready to evolve from bespoke delivery into a repeatable product motion.
Partner Across the Company
Work directly with the GM or Solutions Lead to define the vertical’s technical strategy and commercial approach.
Partner with Data Lab on domain-specific data and research questions.
Collaborate with other FDEs on shared patterns, tools, and approaches that should compound across verticals.
Serve as the technical voice of the vertical as it grows.
What Success Looks Like
The shape of this role depends on the vertical you're deployed into, so we measure success based on trajectory rather than a fixed set of outputs. In the first 90 days, we expect the following to happen:
Build an understanding of the vertical and strategy
Develop a strong understanding of the vertical, the market dynamics, and the GM's strategy for building and scaling the business. Gain context on the customer landscape, commercial motion, and the unique technical requirements that will shape the vertical's success.
Understand customer and platform needs
Build a deep understanding of what customers and data partners need, what Protege's platform can already support, and where meaningful gaps exist. Develop a clear point of view on which constraints are temporary, which require new infrastructure, and which represent opportunities for future product investment.
Identify the highest-leverage technical bets
Evaluate the technical landscape and identify the most important investments that will unlock customer success, accelerate delivery, and create long-term leverage for the vertical. Prioritize decisions thoughtfully, balancing immediate customer needs against durable architecture.
Ship the first version of the vertical's infrastructure
Build the initial technical foundation for the vertical by leveraging the core platform where it fits and extending it where the vertical requires new capabilities. Deliver production-grade systems, integrations, workflows, and tooling that create a foundation future engagements can build upon.
Lead customer engagements end-to-end
Own early customer engagements from technical scoping through delivery and post-launch support. Use learnings from those engagements to continuously improve the underlying infrastructure, implementation patterns, and technical playbook.
Create reuse and drive productization
Successfully reuse the vertical's technical foundation across multiple customer engagements, demonstrating that the systems being built are durable rather than one-off solutions. Surface repeatable patterns, infrastructure gaps, and product opportunities, and partner with Product and Engineering to elevate proven capabilities into the core platform.
What You Bring
Must Haves
3+ years of engineering experience, including meaningful 0→1 work as a founding engineer, early technical lead, or builder in a highly ambiguous environment.
Strong engineering generalist instincts with a backend and data orientation.
Hands-on experience with Python and SQL.
Comfort working across infrastructure, application logic, and data systems.
Ability to create structure where none exists and move quickly without a fully defined roadmap.
Strong technical judgment, especially around short-term delivery versus long-term architecture.
Strong written and verbal communication skills, including the ability to work directly with senior technical and business stakeholders.
Ability to independently run technical scoping conversations with customers and translate them into concrete execution plans.
Nice to Haves
Prior founding engineer experience at a successful startup.
Experience extending an existing platform into a new domain or use case.
Track record of turning customer-specific work into reusable internal infrastructure or product capabilities.
Familiarity with ML, NLP, or LLM-based systems.
Why This Role Is Special
This is a rare opportunity to define the technical foundation of a new business line inside a company that already has a real platform, real customers, and real momentum.
You won’t just be executing against a spec. You’ll help decide what the spec should be. If you enjoy building in ambiguity, working directly with customers, and creating systems that become the basis for an entire vertical, this role is for you.
Protege Values
Pass the Loved Ones’ Test We act with integrity and do the right thing — especially when it’s hard and no one is watching.
Always Find a Way We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor We communicate directly and respectfully, building trust through honest feedback and genuine care for one another. Deliver Together We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft. We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
Forward Deployed Engineer, Healthcare
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
Engineering
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
About the Role
Protege is hiring Forward Deployed Engineers to join our healthcare team. This is a hands-on role focused on end-to-end ownership of customer engagements, from initial feasibility through delivery and post-launch support.
You'll be part of a team that works at the crossroads of customer needs, healthcare data, and our platform. You’ll partner closely with our healthcare GM, Solutions Leads, and DataLab to navigate customer requirements. Within the team, FDEs take different shapes of engagement. Some work across many healthcare deals and customers, while others are dedicated to specific strategic accounts. Every FDE is meant to build the last-mile work that makes deals successful, surface the patterns that should become product, and continue to develop the technical depth that makes us effective in healthcare.
This role is ideal for engineers who want to own customer outcomes as it sits at the center of a vertical that is growing extremely rapidly. You'll be managing customer relationships, technical delivery, and architecture decisions at the same time, across multiple deals at a time. The pace is fast, the ambiguity is real, and when deals are live, availability outside standard hours is part of the job. If this type of environment and growth excites you, there could be a strong fit. If this is not something you are actively looking for, this likely is not the correct role.
What You’ll Do
Own healthcare customer engagements end-to-end
Lead customer-facing technical work from initial scoping through implementation, delivery, and post-delivery support.
Become the primary technical partner for assigned healthcare customers.
Build trusted relationships with customer engineering and technical teams.
Manage multiple concurrent healthcare engagements while maintaining delivery quality and customer confidence.
Build custom solutions for complex customer needs
Develop and deploy integrations, data transformations, and applications that solve customer-specific challenges.
Navigate customer technical environments and adapt Protege’s platform capabilities to real-world deployment constraints.
Translate ambiguous customer needs into clear technical plans, delivery milestones, and practical implementation paths.
Bridge customer needs with product and platform capabilities
Identify where customer-specific work points to repeatable product opportunities.
Surface actionable product feedback based on implementation patterns, customer pain points, and delivery blockers.
Partner with product and engineering to turn bespoke solutions into reusable platform capabilities where appropriate.
Operate with strong healthcare data judgment
Handle healthcare data environments with appropriate care, including PHI and regulatory considerations.
Make sound technical decisions in complex, regulated settings.
Communicate clearly with customers and internal teams about tradeoffs, risks, dependencies, and implementation choices.
Create reusable delivery leverage
Contribute playbooks, tooling, templates, and best practices that improve future healthcare deal execution.
Document repeatable patterns and lessons from customer engagements.
Help Protege move faster over time by turning one-off implementation work into reusable delivery assets.
What Success Looks Like
30 days: Support an active deal
The FDE has built context on Protege’s healthcare customer motion, platform capabilities, common implementation needs, and regulated data considerations. They are supporting an active customer engagement with guidance.
60 days: Lead a solo engagement
The FDE is independently leading a scoped customer engagement or technical workstream, building trust with customer technical teams, and delivering against customer requirements and timelines.
90 days: Own multiple concurrent engagements
The FDE is independently managing multiple healthcare customer engagements, contributing reusable playbooks or tooling, and surfacing product feedback that helps convert bespoke implementation patterns into scalable capabilities.
What You Bring
Experience in software engineering, solution architecture, forward deployed engineering, technical implementation, or a similar customer-facing technical role.
Ability to own customer engagements from scoping through delivery and support.
Strong technical execution across custom solutions, integrations, data transformations, and/or applications.
Comfort operating in healthcare data environments, including sensitivity to PHI and regulatory considerations.
Strong customer-facing communication and ability to build trust with technical stakeholders.
Ability to bridge customer needs with product and platform capabilities.
High ownership, adaptability, and comfort operating in ambiguous, fast-moving environments.
Bias toward creating reusable systems, playbooks, tooling, and best practices rather than solving the same problem repeatedly.
Protege Values
Pass the Loved Ones' Test
We act with integrity and do the right thing - especially when it's hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
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Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
Machine Learning Researcher, Audio
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
DataLab
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
Role Overview
Data is the foundation of AI performance, and we believe model quality starts with data quality. For speech and audio models in particular, the bar for signal fidelity, consistency, and quality control is exceptionally high.
We’re seeking a Machine Learning Researcher focused on audio data quality, ML data evaluation, and quality control to lead the evaluation and optimization of large-scale speech datasets used to train audio, speech, and multimodal models. This role will be responsible not only for applying existing audio quality metrics, but also for researching how audio data quality should be evaluated for machine learning systems and developing new methods, benchmarks, and evaluation frameworks that better predict downstream model performance.
You will help define what “high-quality audio data” means in the context of modern ML training. That includes studying how different forms of acoustic degradation, dataset inconsistency, recording conditions, speaker variation, labeling quality, segmentation quality, and signal artifacts affect model behavior across ASR, TTS, speaker modeling, representation learning, and multimodal systems.
A core part of this role will be original research and method development: designing new approaches for measuring audio data quality, validating those approaches against downstream model outcomes, and translating research insights into practical evaluation tools, filtering rules, and quality standards used across Protege’s data platform.
This is an ideal role for someone deeply obsessed with audio data quality and signal understanding, comfortable operating in both research and hands-on implementation modes, and excited to help Protege become the ubiquitous platform for high-quality AI training data.
What You’ll Do
Research audio data quality for machine learning
Investigate how audio quality, signal properties, dataset composition, and localized acoustic issues affect downstream model training, evaluation, and deployment.
Develop new metrics, benchmarks, diagnostics, and evaluation frameworks for measuring audio data quality in ways that are predictive of ML model performance.
Speech dataset characterization and metrics
Analyze and summarize Protege’s audio catalog and maintain clear, up-to-date quality scorecards and metrics for key speech datasets.
Develop methods to measure true acoustic properties directly from the waveform, including effective bandwidth, spectral energy distribution, high-frequency roll-off, noise, clipping, reverberation, distortion, and codec artifacts.
Segment-level quality evaluation
Build workflows that evaluate diarized or segmented speech regions, surfacing localized degradation that file-level averages may miss.
Apply multiple complementary quality metrics to detect bandwidth mismatches, resampling artifacts, clipping, reverberation, codec distortion, and other forms of degradation.
Model and data evaluation
Design and run targeted evaluations connecting audio quality issues to downstream model behavior, including ASR performance, speaker embedding stability, learned speech representations, and synthesis quality.
Test which audio quality metrics meaningfully correlate with model outcomes, identify failure modes of existing metrics, and design better alternatives when current approaches are insufficient.
Deterministic filtering and evaluation infrastructure
Translate research findings into reproducible filtering rules, quality gates, and dataset selection strategies that improve dataset consistency across training runs.
Build scalable tools and pipelines for applying audio quality analyses across large datasets, tracking results over time, and making quality signals accessible to researchers, engineers, and data teams.
Cross-functional collaboration
Work closely with ML researchers, data engineers, data operations, and external partners to define, measure, and communicate the value of Protege’s audio data assets.
What Success Looks Like
Near-term: establish a trustworthy audio-quality baseline
Create a trustworthy view of the quality, consistency, signal fidelity, and training-readiness of Protege’s speech and audio datasets, supported by metrics and scorecards the team can operationalize.
Then use targeted evaluations, ablations, and downstream model analysis to connect audio-quality issues to concrete dataset improvements and clearer prioritization over time.
What You Bring
PhD or equivalent Master’s degree + 4+ years industry experience in machine learning, audio signal processing, speech technology, computer science, statistics, engineering, or a related quantitative field.
Proven experience designing and running data evaluations, audio analyses, benchmarks, ablations, or slice-based analyses.
Strong understanding of speech/audio data and signal properties, including sampling rates, codecs, bandwidth, spectrograms, reverberation, clipping, noise, and perceptual quality.
Experience developing or critically evaluating metrics, benchmarks, or measurement frameworks for ML systems, data quality, speech technology, or audio signal analysis.
Ability to connect low-level signal properties to downstream machine learning behavior, including model accuracy, robustness, representation quality, speaker consistency, or synthesis quality.
Comfortable moving between research exploration and production implementation: you can formulate hypotheses, run experiments, analyze results, and turn findings into scalable tools or decision rules.
Excellent written and verbal communicator; able to write concise technical docs and explain empirical results clearly.
High ownership and bias toward action; you independently scope questions, design experiments, and drive them to decisions.
Bonus Signals
Experience with ASR, TTS, speaker modeling, self-supervised speech models, diarization, or multimodal audio models.
Experience developing evaluation frameworks or performance metrics for training data.
Experience inventing, adapting, or validating audio quality metrics for ML training datasets.
Experience studying the relationship between dataset quality and downstream model performance.
Publications or open-source contributions in speech, audio ML, data-centric AI, ML evaluation, or related areas.
Cross-functional collaboration with product, infrastructure, data operations, or partnership teams.
Experience collaborating with industry or academic labs on speech/audio research or data projects.
Protege Values
Pass the Loved Ones’ Test
We act with integrity and do the right thing — especially when it’s hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
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Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
Machine Learning Researcher, RL & Agentic Systems
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
DataLab
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
About DataLab
DataLab exists because truly useful data is rare — and the frontier of AI development only moves forward when high-quality data makes it possible.
We believe data is one of the most underdeveloped layers of the AI stack. Our work focuses on building and evaluating high-value datasets grounded in real-world workflows and economically meaningful tasks.
We work across multiple domains to create safe, high-fidelity datasets that preserve the structure and context needed to train advanced AI systems.
Our research spans data quality, evaluation design, privacy-preserving transformation, workflow reconstruction, and task-grounded AI training data.
At DataLab, applied research is tightly connected to real-world deployment. Researchers work directly with large-scale datasets, production systems, and frontier AI training problems.
Role Overview
Data is the foundation of AI performance, and we believe model quality starts with data quality. As AI systems become more agentic, a critical challenge is understanding which real-world datasets, tasks, and environments actually lead to better model behavior.
We’re seeking a Machine Learning Researcher focused on RL and agentic systems to help define, design, and evaluate the datasets, tasks, environments, and benchmarks used to assess advanced AI systems. In this role, you’ll work closely with research and engineering teams to translate real-world workflows into high-value datasets and evaluation assets: structured tasks, interactive environments, benchmark suites, and quality scorecards that help us understand how models perform in realistic settings.
You’ll help define what “high-quality agentic data” means in practice, using statistical, computational, and ML-driven methods to evaluate dataset quality, task design, environment fidelity, and downstream model performance. You’ll work on the core problems of benchmarking real-world data, measuring how well models perform on that data, and designing RL-style or agentic environments that capture the structure of meaningful work.
This is an ideal role for someone with a strong machine learning background who is excited by reinforcement learning, agentic systems, evaluation, and the role of data in shaping model behavior. You should be excited by the opportunity to build the datasets and benchmarks that help define what high-quality real-world data looks like for frontier AI systems.
What You’ll Do
Design and build datasets, tasks, and environments
Design and build datasets, tasks, environments, and evaluation assets for benchmarking agentic systems and multi-step model behavior.
Translate real-world workflows into structured tasks, interaction traces, trajectories, stateful environments, and verifiable outcomes that can be used to evaluate advanced AI systems.
Develop frameworks for evaluating real-world data quality
Develop frameworks that assess diversity, realism, coverage, fidelity, informativeness, and downstream usefulness of datasets for agentic systems.
Build quality scorecards and evaluation methods that make dataset strengths, weaknesses, and failure modes legible across teams.
Benchmark model behavior in RL and agentic settings
Evaluate planning, tool use, robustness, recovery from failure, task completion, and generalization behavior in RL-style or agentic environments.
Connect model failures back to concrete dataset, environment, or task-design gaps and recommend improvements grounded in empirical evidence.
Build scalable evaluation and validation tooling
Contribute to tools and systems that automate dataset validation, environment generation, rollout analysis, benchmark construction, and evaluation workflows.
Improve internal infrastructure for reproducible experimentation, benchmark management, and evaluation quality.
Partner across research, engineering, and product
Collaborate closely with research and engineering teams to identify data bottlenecks, improve evaluation methodology, and shape internal best practices around task-grounded AI training data.
Represent DataLab’s perspective in cross-functional discussions around dataset quality, benchmark design, and frontier agentic-system evaluation.
What Success Looks Like
Near-term: establish a strong evaluation baseline
Create clear benchmark frameworks, evaluation assets, and dataset-quality scorecards that help Protege reason about how real-world data impacts advanced agentic systems.
Use rigorous evaluation methods to identify meaningful dataset improvements, improve benchmark fidelity, and sharpen the company’s understanding of what high-impact agentic data actually looks like in practice.
What You Bring
PhD or equivalent Master’s Degree + 4+ years industry experience in machine learning, computer science, statistics, engineering, mathematics, economics, or related quantitative fields.
Strong understanding of AI model training pipelines, evaluation methodology, and the role of data in shaping model performance.
Experience working with large, unstructured, or semi-structured datasets used to train or evaluate ML systems.
Experience with reinforcement learning, sequential decision-making, agentic systems, tool-using models, or multi-step model evaluation.
Experience designing tasks, benchmarks, environments, simulations, or evaluation frameworks for real-world model behavior.
Strong intuition for realism, coverage, difficulty, fidelity, and meaningful outcome structure in datasets.
Strong experimental design, evaluation, benchmarking, and data-validation skills.
High ownership and ability to independently identify and solve high-impact problems.
Nice to have
Experience developing evaluation frameworks or performance metrics for datasets, agentic systems, or training data.
Experience translating real-world workflows into structured tasks or environments for model evaluation.
Experience with RLHF, RLAIF, imitation learning, reward modeling, online or offline RL, or related methods.
Experience with Harbor or other agent evaluation frameworks.
Publications or open-source contributions in reinforcement learning, agents, evaluation, or data-centric AI.
Experience collaborating cross-functionally with product, infrastructure, or partnership teams.
Experience with synthetic data generation, trajectory generation, or simulation-based environments.
Protege's Values
Pass the Loved Ones' Test
We act with integrity and do the right thing - especially when it's hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
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Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
Head of Security & IT
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
Engineering
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
Purpose
We're hiring our first Head of Security & IT to own security end-to-end: strategy, architecture, operations, and culture. This is a hands-on leadership role and you won't have a large team beneath you (yet), so you need to be comfortable building the program from the ground up while still getting into the technical weeds. You'll report directly to the VP of Engineering and work closely with engineering, product, and legal.
This is a high-impact role where you'll shape how we earn and keep the trust of AI companies and our data partners.
What You’ll Do:
Mature the Security & Compliance Program
Audit and improve the existing security program by identifying gaps, prioritizing improvements, and bringing more structure to what exists.
Formalize security policies and frameworks appropriate for our stage
Own and evolve our compliance posture. We have SOC 2 Type II in place and you'll maintain it, improve our controls, and provide automation wherever needed
Ensure compliance with HIPAA and other healthcare data regulations, and build a robust PHI protection program
Protect the Data Pipeline
Secure the end-to-end lifecycle of training data which includes ingestion, processing, storage, preparation, and delivery
Partner with engineering to embed security into CI/CD pipelines, cloud infrastructure, and data workflows
Be Technical and Hands-On
Conduct threat modeling, architecture reviews, and code-level security assessments
Lead incident response when things go wrong
Evaluate and deploy security tooling
Enable the Business
Translate security risks into business language for the executive team and board
Serve as the security face to customers, fielding security questionnaires, supporting sales cycles, and building trust with AI company partners and customers
Build a security-aware culture across the company through training and lightweight processes that don't slow teams down
Scale the Function
Decide what to build, what to buy, and what to outsource
Set the roadmap for how security evolves from Series A through a rapid growth stage
What Success Looks Like:
30 days: Learn and Assess
Complete a thorough audit of the existing security program, infrastructure, tooling, and policies
Meet with every team lead to understand their workflows, data handling practices, and where security creates friction or blind spots
Review our SOC 2 Type II and HIPAA controls and identify areas where we're passing but brittle vs. areas that are solid
Map the full training data lifecycle end-to-end from a security and risk perspective
60 days: Prioritize and Start Building
Present a security roadmap with quick wins (first 90 days) and longer-term initiatives (6–12 months), tied to business risk, not just best practices
Close the highest-severity gaps identified in your assessment
Upgrade incident response program
Establish yourself as the go-to security partner for engineering
Identify the highest-leverage automation opportunities
90 days: Fully Own
You've taken full ownership of our SOC 2 compliance cycle and have a plan for any additional certifications or frameworks the business needs
You've fielded at least one customer security review or questionnaire and can represent our posture confidently to prospects
The team sees security as an enabler, not a bottleneck
At least one meaningful security workflow has been automated
The security roadmap is in execution with measurable progress
What You Bring:
Must Haves
8+ years in security roles, with at least 2 years in a leadership capacity
Deep technical foundation: you've worked as or alongside engineers and can credibly review architecture, infrastructure, and code
Experience building or significantly maturing a security program at an early-stage or high-growth company (not just maintaining one at a large enterprise)
Strong understanding of cloud security (AWS, GCP, or Azure), identity/access management, and data protection at scale
Hands-on experience with compliance frameworks (SOC 2, ISO 27001). You’ve maintained certifications and know how to expand scope without over-engineering the problem
Hands-on experience with HIPAA compliance
Comfort operating as an individual contributor and a leader simultaneously
Nice to Haves
Experience securing data pipelines or working with data-intensive platforms
Experience working in a data infrastructure company
Background in AI/ML or companies selling to technical buyers
Experience with data provenance, lineage tracking, or data governance in ML contexts
Familiarity with supply chain security
Prior experience as a customer-facing security leader
Protege Values
Pass the Loved Ones’ Test
We act with integrity and do the right thing — especially when it’s hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
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Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
Solutions Engineer, Media
Location
Remote
Employment Type
Full time
Location Type
Remote
Department
Solutions
Overview
Application
Company Overview:
We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy-centric exchange of AI training data.
Solving AI’s data problem is a generational opportunity. We’re backed by world-class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech.
We’re a lean, fast-moving, high-trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI.
Role Overview
We’re hiring a Solutions Engineer for our media vertical to connect Protege’s media catalog with customer AI data needs. This is not a traditional modeling role. It is an applied data curation and delivery role for fast-moving, ambiguous environments where both speed and quality matter.
You will work with imperfect, evolving partner datasets and build strategies to normalize, validate, and operationalize them for downstream AI use cases. You’ll become an expert in Protege’s growing catalog of audio, video, and motion capture content — from longform assets with title-level metadata to clip-level content generated with TwelveLabs embeddings.
At a high level, you will understand what customers are building, identify the content that best fits their needs, and deliver datasets that meet both technical and conceptual requirements, often on tight timelines tied to active deals.
What You’ll Do
Own data quality and curate media datasets
Partner with Sales and Solutions to translate customer requirements into curation strategies
Work with imperfect partner data, including mismatched metadata, schema differences, and incomplete labeling
Normalize and standardize datasets for reliable downstream use
Query and analyze Protege’s media catalog using SQL, internal APIs, and metadata tools to identify relevant content
Build validation checks and workflows to ensure dataset integrity before delivery
Identify, debug, and resolve data quality issues across file structures, metadata, and content alignment
Use AI tools and transcoded embeddings to surface and refine clip-level content
Turn messy, real-world data into structured datasets that meet customer and model requirements
Run iterative sample reviews with customers, incorporate feedback, refine selections, and ensure final packages meet spec
Be the catalog expert
Build deep expertise in Protege’s media catalog structure, metadata, and growth patterns
Track content coverage, diversity, and modality mix, and identify gaps relative to customer demand
Partner with Product and Partnerships to share catalog insights that inform sourcing priorities
Operate across product, data, and customer
Work cross-functionally to ensure content packaging meets technical, ethical, and licensing requirements
Develop methods, scripts, and internal tools that improve curation efficiency and scale
Help shape Protege’s delivery platform, including how internal users and customers search, sample, and export data
Drive human-in-the-loop media search and curation
Work closely with embedding-based systems to iterate between algorithmic selection and human review
Define best practices for embedding queries, relevance evaluation, and content diversity
Maintain a high bar for operational excellence and quality assurance throughout the process
What Success Looks Like
30 days: Learn and get operational
Build a working understanding of the media catalog, delivery lifecycle, and core tools.
Establish strong cross-functional relationships and shadow live curation workflows.
60 days: Deliver and improve
Lead dataset sampling and curation for active use cases, and document reusable workflows.
Surface early insights on catalog coverage, metadata quality, and process improvements.
90 days: Scale and influence
Create repeatable QA and delivery workflows that increase consistency and speed.
Provide actionable feedback that shapes platform, sourcing, and catalog roadmap decisions.
What You Bring
4-7 years of experience in data science, media analytics, technical curation, or similarly hands-on data roles.
Strong SQL proficiency and comfort querying large, messy datasets to generate insight and action.
Experience working with media metadata, embeddings, or unstructured content.
Ability to translate nuanced customer or model requirements into concrete dataset specifications.
High standard for data quality, operational rigor, and usability of delivered outputs.
Clear communicator who can move between technical depth and customer-friendly clarity.
Thrive in ambiguous, fast-moving environments and treats teammates with kindness.
Bonus if you also have:
Familiarity with video/audio processing, embeddings, or multimodal AI workflows.
Prior experience curating or packaging datasets for machine learning.
Background in content analysis, recommendation systems, or information retrieval.
Protege Values
Pass the Loved Ones’ Test
We act with integrity and do the right thing — especially when it’s hard and no one is watching.
Always Find a Way
We are resourceful, resilient builders who solve hard problems and push through obstacles.
Go Fast and Grow Fast
Velocity matters. We move with urgency, learn quickly, and continuously improve as individuals and as a company.
Practice Kindness and Candor
We communicate directly and respectfully, building trust through honest feedback and genuine care for one another.
Deliver Together
We win as one team. Collaboration, accountability, and shared ownership drive our success.
Own the Outcome. Hone the Craft.
We take pride in our work, sweat the details, and continuously raise the bar for excellence.
Apply for this Job
Powered by
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Listed by Protege for a position based in the United States. Employers on this board attest they are hiring domestically.
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