Research Engineer

Judgment Labs builds infrastructure for Agent Behavior Monitoring (ABM). While traditional observability focuses on logging exceptions and latency, our ABM surfaces behavioral anomalies such as instruction drifts and context retrieval loss in scaled production environments.

Hundreds of teams building autonomous agents rely on Judgment to understand how their systems are behaving post-deployment. Instead of reactive incident triage, they cluster patterns across conversations and workflows, correlate regressions to specific interaction types, and pinpoint where reliability breaks down in their usage context.

We’ve raised $30M+ across two rounds in the past five months. Our investors include Lightspeed, SV Angel, Valor Equity Partners, Nova Global, Chris Manning, Michael Ovitz, Michael Abbott, Cory Levy, Kevin Hartz, and others.

We are looking for Research Engineers to build AI systems that use agent interaction data to help us 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 immediately 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.

• Build systems to aggregate, index, and analyze large-scale agent interaction data to extract meaningful evaluation signals

• Develop agent-based systems for analyzing and evaluating complex, long-running behaviors

• Design and implement post-training and optimization workflows to improve agent behavior

• Build internal tools and infrastructure to support rapid experimentation, analysis, and training

You identify with at least one of the following:

• You care about data quality, evaluation, and benchmarking, and are comfortable working hands-on with messy data

• You have experience building agent systems and working with them in real-world or production settings

• You have a strong background in reinforcement learning, agents, or machine learning fundamentals

• You are comfortable working across infrastructure and systems, spanning training, data pipelines, and model serving.

• You are comfortable working across teams to translate research into product, balancing real-world customer constraints and tradeoffs.

• You enjoy turning ambiguous problems into clear, well-designed plans

• Agents can’t work without this. Today’s agents hallucinate, drift, and break in production. We’re building the infrastructure that fixes this: the monitoring layer that makes agents self-improving.

• We’re wired to win. We're a team of less than 20 but we ship like 50+ on the daily. You'll be working with olympiad medalists, debate champions, and competitive athletes who bring that same intensity to company building.

• Fast track to founding. Our engineers interface directly with customers, ship code into their environments, and use their feedback to dictate what’s next on the roadmap. Everyone on the team is either an ex-founder or a founder-to-be.

• We make sure our people do their best work. If you deserve a spot on the team, money will never get in the way of it. Full benefits, Equinox, and a private chef to take care of you. We sprint hard but we play hard, ask us about our Smash/Mario Kart tournaments.

  We work in person in San Francisco.