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.