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Founding Account Executive
Judgment Labs · San Francisco, California, United States
$70k–165k
55 days ago
♡
Full stack engineer
Judgment Labs · San Francisco, California, United States
$95k–220k
57 days ago
♡
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Founding Account Executive
Judgment Labs · San Francisco, California, United States
Pay
$70k–165k
Setting
On-site
Founding Account Executive
Location
San Francisco
Employment Type
Full time
Location Type
On-site
Department
Growth/Operations
Overview
Application
Role Overview
We are hiring a full-cycle Account Executive who will own both pipeline generation and closing.
As an early GTM hire, you will have significant ownership, meaningful equity, and a direct role in building our commercial organization from the ground up. You will work closely with founders, engineers, and FDEs to turn early customer traction into a repeatable GTM motion.
This role is for someone who wants to build the sales organization—not inherit an established playbook.
What You’ll Do
Build qualified pipeline through outbound
Create, run, monitor, and improve outbound sequences
Lead discovery and qualification calls
Own prospects from initial conversation through pilot, negotiation, and close
Partner with FDEs during onboarding, kickoff, and deployment
Maintain customer relationships and identify expansion opportunities
Develop messaging, sales collateral, qualification criteria, and sales processes
Translate customer feedback into useful insights for product and engineering
Help shape how we hire, train, and structure the future GTM organization
What Success Looks Like
Consistently creates and closes qualified pipeline
Builds credibility with engineering and technical buyers
Moves customers efficiently from discovery through deployment
Identifies and closes expansion opportunities
Improves the systems and messaging used throughout the sales process
Takes ownership beyond an individual revenue target
Helps turn early customer traction into a scalable commercial organization
What We’re Looking For
At least one year of full-cycle sales experience spanning pipeline generation and closing
Personally created outbound pipeline rather than relying entirely on SDRs or inbound
Experience selling technical products to engineering or similarly technical buyers
Experience selling to startups
Familiarity with high-velocity, usage-based, or pay-as-you-go sales motions
Strong written and verbal communication
Ability to operate effectively without an established sales playbook
Strong reasoning and the ability to explain why a sales approach succeeded or failed
Genuine interest in meaningful ownership, equity, and company-building
Willingness to work from our San Francisco office five days per week
Nice to Have
Developer tools, infrastructure, data, or AI sales experience
Founding AE or early GTM experience
Experience working closely with technical implementation or deployment teams
Evidence of resilience in difficult or ambiguous environments
Independent or unusual thinking
Experience and Seniority
We care more about relevant experience, ownership, learning velocity, and slope than title or total years of experience.
Location
San Francisco, in office five days per week.
Compensation
Competitive base salary, performance-based variable compensation, and meaningful equity.
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Full stack engineer
Judgment Labs · San Francisco, California, United States
Pay
$95k–220k
Setting
On-site
Product engineer, full stack
Location
San Francisco
Employment Type
Full time
Location Type
On-site
Department
Engineering
Overview
Application
Product Engineer — Full Stack Job Description
The Role
Judgment is the learning infrastructure for AI agents. Agents in production don't improve from prompts alone. They improve from experience: the tasks they attempt, the mistakes they make, the edge cases they hit. Here's how it works:
We ingest everything your agents do in production: traces, tool calls, decisions, outcomes
Judgment turns that raw experience into structured signals: failure modes, behaviors, rubrics, evals
Teams close the loop, shipping agent improvements validated against real production evidence
You'll own problems end-to-end: talking to customers, defining what to build, building it, and iterating until it's great. This is not a role where you implement specs handed down.
What You Will Accomplish
Investigation interfaces: Design how engineers understand what their systems did and why. Long traces, tool calls, decisions, failures. How do you make a complex sequence of events legible in minutes?
Verification: Build the platform for verifying system changes: hosted simulated environments, trajectory replay, and monitors for unintended behavior changes.
The improvement loop: Build the workflows that turn production data into datasets, evaluations, and regression checks, so the path from "found a problem" to "verified a fix" feels like one motion.
The platform underneath: Workspaces, roles, permissions, billing, usage, and limits for teams running many workflows across many environments.
What You'll Bring
Experience building and scaling end-to-end production systems, from data layer to UI
Strong technical problem-solving skills, especially in fast-changing, ambiguous environments
A builder and tinkerer's mindset with high agency - you find creative ways to overcome obstacles and ship
Comfort working directly with customers to understand their needs and solve real-world problems
Excellent communication skills - clear, direct, and persuasive across technical and non-technical audiences
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Senior Backend Engineer
Judgment Labs · San Francisco, California, United States
Pay
$165k–240k
Setting
On-site
Backend/Infra Engineer
Location
San Francisco
Employment Type
Full time
Location Type
On-site
Department
Engineering
Overview
Application
Senior Backend Engineer
San Francisco · On Site · Full Time
Judgment Labs is building the infrastructure for continual learning in long-horizon AI agents.
The next generation of agents will not improve from prompts alone. They will improve from experience: the tasks they attempt, the tools they use, the mistakes they make, the edge cases they encounter, and the outcomes they produce in production. The hard part is turning that raw experience into high-quality data that can actually improve the system.
Judgment builds the infrastructure to do that. We turn long agent trajectories into clean, structured data for evals, labeling, rubric generation, context engineering, and RL workflows. Instead of only showing teams what happened, Judgment helps decide what matters, what should be learned from, and how that learning should flow back into the agent.
Databricks built the data infrastructure for analytics. Judgment is building the learning infrastructure for agents.
We’ve raised $30M+ from Lightspeed, SV Angel, Valor Equity Partners, and others.
The Role
We’re looking for a Senior Backend Engineer to own the systems that ingest, structure, evaluate, and serve agent experience data at production scale.
This role includes the backend and data infrastructure surface area: high-throughput telemetry ingestion, ClickHouse-backed OLAP performance, evaluation pipelines, RabbitMQ/Temporal workflows, multi-tenant scheduling, and product-facing APIs. Some weeks you’ll be deep in distributed systems and query performance. Other weeks you’ll ship a customer-facing feature end to end across backend, frontend, and the data layer.
This is not a narrow API role. The backend is where raw agent trajectories become structured learning data.
Interesting Technical Challenges
High-throughput telemetry ingestion. Parse and persist OTEL traces across protobuf and JSON formats at hundreds of thousands of spans per second, writing to ClickHouse while keeping ingest latency low and backpressure graceful as customer traffic spikes.
Petabyte-scale OLAP performance. Design schemas, partitioning, indexes, storage layouts, and query paths so behavioral queries over billions of spans stay fast. Turn real access-pattern analysis into concrete data modeling decisions.
Long-horizon trajectory modeling. Agent workflows are messy: multi-step tasks, tool calls, retries, partial failures, context changes, and unclear outcomes. Build the abstractions that turn those trajectories into structured data for evals, labeling, rubric generation, context engineering, and RL workflows.
Queue- and workflow-driven evaluation. Evaluations fan out across RabbitMQ and Temporal workflows. Getting this right means reasoning about retries, timeouts, idempotent state transitions, exactly-once-ish semantics, and reconciling runs that fail partway so nothing is silently orphaned.
Multi-tenant fairness at scale. A single large customer should not be able to starve everyone else. Build scheduling and execution systems so latency stays predictable across hundreds of teams sharing the same evaluation pipeline.
Near-real-time scoring. Behavioral scorers and agent judges call LLM APIs at scale, so batching, rate-limit management, retry/backoff, failure handling, and cost control are core backend systems problems.
Learning loops for agents. Build the product and systems layer that helps teams decide what matters, what should be learned from, and how that learning flows back into the agent.
What You’ll Do
Design and build backend systems for trace ingestion, trajectory processing, evaluation orchestration, scoring, labeling, rubric generation, and customer-facing analytics.
Own the API surface used by the Judgment platform UI, SDKs, JudgmentHub libraries, MCP server, Slack agent, and customer integrations.
Build and operate the RabbitMQ / Temporal evaluation pipeline, including retry semantics, failure recovery, state reconciliation, and tenant-level scheduling.
Optimize the ClickHouse OLAP layer: schema design, partitioning, skip indexes, full-text-search pruning, query rewrites, deduplication, pagination correctness, and storage growth.
Turn raw spans, conversations, tool calls, scorer outputs, and agent-judge results into clean data models customers can use for evals, labeling, context engineering, and RL workflows.
Ship features end to end, often across Next.js, backend APIs, queues/workflows, and the data layer.
Work directly with customers to understand where their agents fail, what data is useful, and how Judgment should structure that experience for learning.
Roll out safely with feature flags, design docs, code reviews, tests, observability, and production debugging.
Raise the engineering bar through clear interfaces, maintainable systems, thoughtful reviews, and strong ownership.
What We’re Looking For
Strong backend engineering experience building and operating production systems under real load.
Excellent fundamentals in distributed systems, API design, data modeling, reliability, and performance.
Experience working with high-volume event, trace, log, metric, or telemetry data.
Strong intuition for data systems: query patterns, storage layout, indexing, partitioning, latency, correctness, and cost.
Comfort owning systems beyond initial launch: debugging production issues, improving observability, scaling bottlenecks, and cleaning up abstractions as the product evolves.
Ability to work across backend, data, product, and infrastructure boundaries rather than treating them as separate silos.
Product judgment and willingness to ship across the stack when needed.
Clear communication. You can write a design doc, review a diff, explain a tradeoff, and unblock others without turning everything into process.
Nice to Have
Experience with ClickHouse, OLAP systems, distributed query engines, or large-scale analytical databases.
Experience with RabbitMQ, Temporal, Kafka, Spark, Flink, Ray, Airflow, Dagster, Prefect, or similar queue/workflow/data systems.
Experience with OTEL, observability products, tracing, logging, or monitoring infrastructure.
Experience building systems that call LLM APIs at scale, including rate-limit management, retries, batching, and cost control.
Experience with LLM evaluation, labeling systems, rubric generation, context engineering, RL data pipelines, embedding pipelines, vector search, clustering, or anomaly detection.
Experience building developer-facing products, SDK-backed platforms, or customer-facing infrastructure.
Why Judgment?
We’re building the learning infrastructure for agents. As agents move from demos to production, the bottleneck is no longer just better prompts. It is turning real production experience into high-quality data for evals, labeling, rubric generation, context engineering, and RL workflows.
The technical problems are foundational. Long agent trajectories are messy, high-volume, and hard to reason about. We’re building the systems that ingest them, structure them, evaluate them, surface what matters, and feed that learning back into the agent.
This is a Databricks-scale infrastructure opportunity. Databricks built the data infrastructure for analytics. Judgment is building the learning infrastructure for agents.
You’ll work on problems customers actually feel. Engineers talk directly to teams building production agents, see where their systems fail, and turn those failures into product and infrastructure.
Small team, high ownership. You will not own a narrow slice. You’ll shape core systems early, ship quickly, and work across product, data, backend, infra, and customer environments.
In person in San Francisco. We work together in person because the problems are hard, the product is moving fast, and the feedback loops matter.
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Research Engineer
Judgment Labs · San Francisco, California, United States
Pay
$95k–320k
Setting
On-site
Applied AI Engineer
Location
San Francisco
Employment Type
Full time
Location Type
On-site
Department
Research
Overview
Application
The Role:
We are looking for Applied AI Engineers to build AI systems that use agent interaction data to understand how agents behave, evaluate them at scale, and improve them through learning and feedback.
Your research will not live on a whiteboard. You’ll work directly with real-world agent data, apply frontier methods in production, and see your work ship into the product. By making agent behavior measurable and debuggable, your systems will support teams deploying agents across finance, legal, operations, and other high-stakes workflows. You will own projects end-to-end, with significant autonomy, and work closely with the team to build self-improving agent systems.
What You'll Do:
Build AI systems to aggregate, index, and analyze large-scale long-running agent interaction data in order to extract meaningful signals
Design and implement post-training and optimization workflows to improve agents, both internally and for customers
Build agent platform infrastructure, including orchestration, runtimes, and developer tools that help teams define, test, deploy, and iterate on complex agent workflows
Build internal tools and infrastructure that support rapid experimentation, analysis, and training
Work closely with product to integrate agents into customer-facing workflows
Collaborate with external companies and research partners on frontier AI research
What We're Looking For
Every hire clears three bars, no exceptions:
Agency. You are intellectually curious, self-directed, and stay up to date with the latest research, blogs, trends, and ideas.
Depth of thought. You can reason clearly about abstract systems, and ideally have experience working on agents, RL, or the infrastructure that supports them.
Ownership. You own outcomes, not just tasks. You use freedom to experiment responsibly, make business-driven decisions, and focus first on work that moves the company forward.
More specifically, you should bring strength in at least one of the following areas:
Data quality, evaluation, benchmarking, and hands-on work with messy production data
Agent systems built or evaluated in real-world or production settings
Reinforcement learning, post-training, agents, or machine learning fundamentals
Infrastructure and systems work across training, data pipelines, evaluation, or model serving
Translating research into product while balancing customer constraints, technical tradeoffs, and business impact
Turning ambiguous problems into clear, well-designed plans
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