Applied AI Engineer

Judgment Labs; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Judgment Labs in San Francisco seeks Applied AI Engineers to build AI systems that analyze agent interaction data, measure behavior, and scale learning and feedback into production.

You will own end-to-end projects with real-world data, collaborating with product teams to deploy self-improving agent workflows and frontier AI research across finance, legal, and operations. Significant autonomy and impact on product direction are offered.

Qualifications

• Experience with real-world agent data and production systems.
• Familiarity with RL concepts and post-training workflows.
• Ability to design and optimize large-scale data pipelines for experiments.

Responsibilities

• Build AI systems to aggregate, index, and analyze large-scale long-running agent interaction data.
• Design post-training and optimization workflows to improve agents.
• Develop agent platform infrastructure including orchestration, runtimes, and developer tools.
• Create internal tools and infrastructure for rapid experimentation, analysis, and training.
• Collaborate with product to integrate agents into customer-facing workflows.
• Partner with external researchers on frontier AI research.

Skills

Data quality

Evaluation

Agent systems

Reinforcement learning

Infrastructure

Job description

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