Beacon is acquiring and operating a portfolio of vertical SaaS companies. Most private equity firms scale by adding people. We are building Beacon to scale by adding software. The thesis is simple. Portfolio operations, value creation, and deal sourcing are bottlenecked by human attention, and the right software platform can lift that ceiling by an order of magnitude.
We are building that platform. A cross-portfolio datalake on open table formats, with a feature store on top that makes the data usable by both people and software. An action layer that runs workflows across three domains: how we run the portfolio, how we grow the portfolio, and how we acquire into it. A feedback loop underneath that captures every action and outcome with stable identifiers. By the next phase of buildout we will have 100+ portfolio companies running on this platform. That is a problem set with serious data scale, real multi-tenant isolation requirements, and very few precedents to copy from.
Beacon has raised $550M+ from investors including General Catalyst, Lightspeed, D1 Capital, CPMG, and the family offices of the founders of Stripe, DoorDash, and Ramp.
About the Role
Members of Technical Staff (MTS) are the senior engineers who build the platform that everything else at Beacon runs on. You will own a piece of the core stack end-to-end: design, implementation, operations, and the long-term technical direction of that area. This is a Staff Engineer role in everything but name. We run flat.
The work is systems engineering at its core. Multi-tenant data infrastructure across very different portcos. Event-driven pipelines that have to be correct under partial failure. Service architectures that have to stay simple as the product surface grows. APIs and SDKs that other engineers — including FDEs out in the field — will build on every day. ML and agentic systems are part of the stack. They sit on top of a foundation that has to be solid first.
This is not infrastructure for its own sake. The platform has to be solid before anything else at Beacon works. That is the job.
What You'll Do
You will own one of these areas end-to-end:
Data platform. The cross-portco data lake on Iceberg with Snowflake or Databricks as the query engine. Per-portco S3 and KMS isolation. The ingestion pipeline from QuickBooks, HubSpot, Salesforce, PostHog, Intercom, Linear, Slack, Gmail, Postgres, Stripe, Zendesk, and our internal tools. The canonical data model that survives contact with very different portcos. The catalog and semantic layer on top so a query like "show me sales across all portcos" actually resolves.
Core services and APIs. The backend services that everything else at Beacon depends on: identity, access control, audit, workflow orchestration, the internal APIs that FDEs and ops engineers build against. The bar here is not novelty. It is correctness, latency, observability, and the kind of API design that ages well.
Multi-tenant isolation. Per-portco data, compute, and credential boundaries. Cross-cloud (AWS and Azure) connectivity. Regional residency for portcos in regulated verticals. This is the unglamorous infrastructure work that determines whether we can onboard portco 50 as fast as portco 5.
Workflow and action runtime. The execution layer that runs operational workflows across the three domains. Typed action surfaces, idempotency, retries, rollback paths, human-in-the-loop approval gates, audit trails. Some workflows are scripted. Some are model-driven. The runtime treats them as variations of the same primitive.
Observability and evals. The harness that tells us whether the system is working: traces, metrics, structured logs, replay infrastructure, regression suites, the ability to safely A/B-test changes across the portfolio. Both for traditional services and for model-driven workflows.
Safety and blast radius. Wrong actions against a portco's customers, revenue, or product are the worst kind of mistake we can make. Designing the autonomy tiers, the kill switches, the per-action-class blast-radius caps, and the audit surfaces is foundational platform work, not an afterthought.
Who You Are
Senior engineering depth. Staff or principal-equivalent. You have built and operated systems that real businesses depend on. You write clean, idiomatic code in at least one of Python, Go, Rust, or TypeScript, and you can work in any of them. You have an opinion on how to structure a service and you can defend it without raising your voice.
Distributed systems intuition. You have lived through enough production incidents to know where things actually break. Idempotency, partial failure, retry semantics, eventual consistency, schema evolution, multi-tenant isolation. These are not concepts you read about. They are things you have debugged at 2am.
Data infrastructure experience. You have built or operated something non-trivial on a modern data stack: Kafka, Spark, dbt, Iceberg, Snowflake, Databricks, BigQuery, or comparable. You understand the difference between a warehouse and a lake, and when each is the right answer.
Platform mindset. You build for the engineer two seats over as much as for the end user. Your APIs are easy to use correctly and hard to use incorrectly. You write the documentation. You make the migration path obvious. You treat developer experience as a feature, not a chore.
Comfortable with ambiguity. The product surface and the scope of the platform are still being defined. You will be making decisions in week 1 that constrain what is possible in year 3. You need to be the kind of engineer who is energized by that, not paralyzed by it.
Interest in modern ML, not necessarily expertise. You do not need to be an ML researcher. You need to be the kind of engineer who can read a paper, build the infrastructure around a model someone else trained, and have an informed opinion on where ML belongs in the stack and where it does not. If you have shipped LLM-driven systems in production, that is a plus, not a requirement.
Bonus Points
Prior Staff or Principal Engineer experience at a high-bar engineering org.
Experience with Iceberg, Polaris, Snowflake, or Databricks at scale.
Multi-tenant SaaS or platform infrastructure background.
Production experience with LLM-driven systems, including evals and observability.
Background in offline RL, contextual bandits, or sequential decision-making (for the applied research workstreams).
Open-source contributions to data infrastructure, observability, or developer tooling projects.
Our Values at Beacon Software
: We acknowledge that the path to getting to the right answer involves being wrong along the way. We have strong beliefs which are weakly held. We actively seek new ideas and believe we can learn from anyone at any time.
: We are truth seeking in our approach to business problems. Business is a repeat game and we believe that human relationships generate alpha. We understand that trust is earned over a lifetime and can be lost in an instant.
: We play to win. We hold ourselves to high standards and will not be outworked. We take pride in having a deep sense of responsibility to ourselves, each other, our partners, and our customers. We believe to whom much is given much is expected.
: We seek to build a generational software company. This will take decades. We manage our expectations and those of our partners to take advantage of the 8th wonder of the world - compounding growth.
How We Use AI in Our Hiring Process: To ensure transparency, we want candidates to know that Beacon Software uses Artificial Intelligence and AI-enabled tools to assist with screening, reviewing, organizing and highlighting profiles and applications that match the key requirements for each role.
AI does not make hiring decisions: Every application is reviewed by a member of our team, and all decisions throughout the process are made by humans. We use AI to support efficiency and consistency, not to replace human judgment. We are committed to a fair, thoughtful, and equitable experience for every candidate.
Listed by Beacon Software for a position based in the United States. Employers on this board attest they are hiring domestically.
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Staff Software Engineer, AI Data Platform
San Francisco Bay Area
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Shape the Future of AI
At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.
About Labelbox
We're the only company offering three integrated solutions for frontier AI development:
Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling
Why Join Us
High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.
Role Overview
Labelbox is the RL data factory for advancing frontier agent capabilities. We build the data, evaluations, and infrastructure that frontier labs use to train and judge their agents. We're looking for talented, experienced engineers to join us. The bar is high: engineers who have strong judgment and set technical direction, quickly build prototypes that scale into the reliable systems, and are at the frontier of agent-first engineering practices and innovating to accelerate the speed of the business.
What you may work on
Eval systems that run millions of agent trajectories to measure model and product quality.
Fine-tuning pipelines that turn evaluation signals into measurable agent improvements.
Agent-first product surfaces: UX and infrastructure for workflows where the user is a model or an agent operator.
The systems behind hundreds of thousands of AI interviews used to source and match freelance workers to projects.
Infrastructure that scales to the throughput frontier labs actually need.
Integration of the latest models and capabilities into production within days of release.
What we're looking for
4+ year track record of shipping systems customers and other engineers rely on
You build full stack prototypes fast and they hold up. The v1 you ship becomes the foundation the rest of the team builds on.
Strong system and API design judgement
Hard architecture and product calls land with you. You make them, defend them under pressure, and update fast when someone else is right.
You ship production code with coding agents daily. You know where they break and what it takes to make them reliable to further accelerate the team's velocity.
You set direction by being the example. Other engineers reach for your designs and your code as the reference.
You move fast in ambiguous, startup-pace environments with influence over authority.
You have worked in all parts of the stack
Deep proficiency in TypeScript and/or Python.
Nice to have
Production experience building LLM- or agent-driven products.
Designing evaluations for LLMs and agents, or producing high-quality data for ML systems.
Background in production distributed systems, ML infrastructure, or data systems at scale.
Our Technology Stack
Our engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency:
Frontend: React.js with Redux, TypeScript
Backend: Node.js, TypeScript, Python, some Java & Kotlin
APIs: GraphQL
Cloud & Infrastructure: Google Cloud Platform (GCP), Kubernetes
Databases: MySQL, Spanner, PostgreSQL
Queueing / Streaming: Kafka, PubSub
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Annual base salary range
$250,000 - $280,000 USD
Life at Labelbox
Location: Join our dedicated tech hub in San Francisco
Work Style: Hybrid model with 3 days per week in office, combining collaboration and flexibility
Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
Growth: Career advancement opportunities directly tied to your impact
Vision: Be part of building the foundation for humanity's most transformative technology
Our Vision
We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs.
Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.
Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.
Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
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Forward Deployed Engineering Manager
San Francisco Bay Area
Apply
Shape the Future of AI
At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.
About Labelbox
We're the only company offering three integrated solutions for frontier AI development:
Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling
Why Join Us
High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.
The role
The FDE Manager leads and grows the team of Forward Deployed Engineers who own the high-level technical side of our customer data programs. FDEs scope tasks, design the pipelines and measurement that turn a customer's goal into a training signal, write the instructions that guide Alignerrs, and work out what each customer actually needs from their data. The FDE Manager owns the people who do that work — their craft, their growth, and how they're deployed across domains and customers — and is accountable for the technical quality and consistency of what the team produces.
This role sits at the intersection of people leadership, technical depth, and delivery quality. The FDE Manager must understand the technical substance of our projects well enough to coach on scoping and pipeline design, pressure-test instructions, judge whether a project's data will genuinely move the customer's model, and raise the bar on measuring quality early rather than late. It's a player-coach role: you lead people, but you stay close enough to the work to set and defend the craft bar yourself.
The FDE Manager reports to the Services lead and partners closely with the SPL Manager, Deployment Leads, and General Managers. A core part of the role is keeping FDEs at the right altitude — focused on higher-level technical and customer-facing work — and actively handing the day-to-day running of projects to the SPLs and Pod Leads, so the team's most expensive technical talent is never absorbed into project operations.
The FDE Manager also owns FDE onboarding and is the steward of the FDE career path, which runs from FDE to FDE 2 to FDE Manager, with branches toward the Forward Deployed Researcher (FDR) track and, in time, toward General Manager.
What You'll Do
Lead the FDE team end-to-end: hire, coach, manage performance, and develop careers across the FDE track and toward FDR or GM.
Own the supply side of FDE staffing: commit FDEs to the staffing cadence and match them to projects by skill and development need, balancing each FDE's preferred vertical with where the work is, and staffing to the phase of a project rather than parking people for its full length.
Set and uphold the craft bar: sharp task scoping, sound pipeline and measurement design (including the LLM-as-judge and quality instrumentation that surface problems early), clear instruction writing, and compelling customer-facing presentation of findings.
Protect FDE focus: keep day-to-day project operations with the SPLs and Pod Leads, and keep FDEs on scoping, technical depth, and what the customer needs from the data.
Own FDE onboarding and the bar that certifies a new FDE as ready to be staffed: define which projects are eligible to onboard on, maintain the instruction and Loom repository, and run the onboarding program — including the core exercise (read a past project's instructions, explain them back, and write a new version in the repo).
Drive reuse and leverage: build the templates, tooling, and playbooks that stop FDEs rebuilding pipelines and instructions from scratch each project, so the team's capacity compounds as we scale.
Ensure FDEs work hand-in-glove with FDRs on research, efficacy, and customer needs, and partner with whoever owns quality sign-off so quality is caught in flight, not at delivery.
Partner with the SPL Manager, Deployment Leads, and GMs on staffing, delivery, and alignment with customer objectives.
Step in on escalations when a pipeline, delivery, or customer relationship is at risk.
Maintain a clear, live view of team capacity, utilization, and bench across active projects.
What You'll Own
The capability and craft bar of the FDE team.
How quickly and consistently new FDEs reach a staffable standard.
Healthy deployment — the right FDEs on the right projects, at the right altitude and utilization.
A growing bench of FDEs developing toward FDR and future leadership.
What We're Looking For
A strong forward-deployed / FDE background, or significant experience managing technical or delivery people — and readiness to be a hands-on, player-coach manager.
Strong technical fluency in our domain: enough depth in frontier-data work, RL environments, data pipelines, and quality/evaluation to coach credibly on scoping, pipelines, judge design, and data quality.
Excellent judgment on what makes data genuinely useful to a customer — how to translate ambiguous requirements into clear plans, and how to tell whether data will actually move a model.
A track record of developing people and giving direct, useful feedback.
A high bar for quality paired with the ability to deliver against ambitious timelines.
Comfort operating in ambiguous, fast-scaling environments where the processes are still being built.
The ability to manage multiple people and projects at once without losing attention to detail.
Nice to Have
Direct experience with RLHF, reinforcement-learning environments, evaluation/benchmark work, or LLM-as-judge systems.
Experience working with forward-deployed engineers, solutions engineers, or implementation teams.
Experience building onboarding programs, instruction systems, or training content.
Experience scaling a team and its operating processes in a high-growth environment.
What Success Looks Like
In your first several months, you'll take ownership of the FDE team, raise the bar on scoping, pipeline, and instruction quality, and get onboarding running smoothly — eligible projects defined, the instruction and Loom repository in good shape, and new FDEs reaching a staffable standard faster and more consistently. You'll build strong working relationships with the SPL Manager, Deployment Leads, and GMs, keep FDEs well-deployed and at the right altitude, and become the person the team relies on for craft and career growth.
Over time, you'll define how FDEs work at scale: the templates, tooling, and playbooks that let the team produce more without rebuilding from scratch, a measurement-and-quality craft bar that surfaces problems early, and a pipeline of FDEs growing into FDRs and future leaders.
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Annual base salary range
$190,000 - $250,000 USD
Life at Labelbox
Location: Join our dedicated tech hub in San Francisco
Work Style: Hybrid model with 3 days per week in office, combining collaboration and flexibility
Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
Growth: Career advancement opportunities directly tied to your impact
Vision: Be part of building the foundation for humanity's most transformative technology
Our Vision
We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs.
Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.
Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.
Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
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Listed by Labelbox for a position based in the United States. Employers on this board attest they are hiring domestically.
Senior Software Engineer, AI Runtime, Mountain View, California; San Francisco, California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff Software Engineer, AI Runtime, Mountain View, California; San Francisco, California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Senior Software Engineer, AI Runtime, Mountain View, California; San Francisco, California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff Software Engineer, AI Runtime, Mountain View, California; San Francisco, California. Join us! Together we can use data to solve the challenges of tomorrow
Listed by Databricks for a position based in the United States. Employers on this board attest they are hiring domestically.
Aledade's AI Enablement team enables, educates, and supports the Product, Tech, and Analytics (PTA) organization in adopting developer-centric AI tools (Claude Code, MCP, plugins, skills, hooks). The Forward-Deployed Engineer (FDE) embeds directly with a PTA cohort — Point of Care, Risk, Data, or another team — to unblock AI adoption on the ground: shipping plugins and skills tailored to that team's workflows, integrating their tools through the MCP Gateway, and turning hard-won lessons into reusable patterns for the broader platform. This is the role for engineers who think like internal consultants: meet teams where they are, build what they need today, and feed durable wins back into the marketplace and installer that the rest of Aledade depends on.
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Primary Duties:
Embed with a PTA cohort and ship targeted enablement. Pair with engineers, PMs, and analysts on the assigned team; build plugins, skills, hooks, and connectors that solve their highest-friction workflows; measure adoption and impact.
Productize wins into the platform. Generalize cohort-specific work into reusable plugins, skill templates, and patterns published to the Claude Code Plugin Marketplace; document authorship guides; reduce the activation cost for the next cohort.
Integrate new tools through the MCP Gateway + AWS connector layer. Add and harden MCP server integrations (Glean, Slack, Jira, Snowflake, Salesforce, Databricks, etc.) to the gateway; partner with security and platform owners on auth/scopes/observability.
Feed back to the platform team and AI Unlock program. Contribute to roadmap and quarterly milestones; participate in office hours and brownbags as a practitioner; surface blockers (security, BAA, cost, throttling) early.
Support adoption health. Help close the long tail of repos with no .claude/ config; mentor cohort engineers on agentic-coding patterns; on-call rotation for marketplace-published artifacts.
Minimum Qualifications:
BS/BTech (or higher) in Computer Science, Engineering or a related field.
3+ years professional software engineering experience
Production experience with at least one modern application stack (Python, TypeScript/Node, Go, or similar) and modern CI/CD.
Demonstrated ability to ship end-to-end in unfamiliar codebases — e.g., consulting, forward-deployed, solutions, or platform-adjacent backgrounds.
Direct hands-on experience with one or more agentic coding tools in a production or near-production setting (Claude Code, Cursor, Cody, Copilot agents, Aider, or equivalent).
Strong written communication: comfortable producing documentation, runbooks, and educational artifacts for engineers who weren't in the room.
Preferred KSA’s:
Experience authoring or maintaining MCP (Model Context Protocol) servers, Claude Code plugins, skills, hooks, or comparable LLM-tooling integrations.
Background in healthcare technology, HIPAA-regulated environments, or PHI-handling systems.