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We are looking for research engineers to build the research pipeline connecting real AI usage to what models learn next.

Create new frontier training environments and task formats from production agent traces.
Analyze agent failures and run experiments to measure training value, working directly with frontier researchers on new benchmarks and task formats.
Build core infrastructure for automated creation, quality control, and evaluation of training environments at scale.

Published agent benchmarks or experience scaling synthetic data or post-training pipelines.
Strong engineering ability: you can turn a research idea into a working system and produce results others can reproduce.
Good experimental judgment: you can distinguish model failures from problems in the data, environment, or evaluation.

San Francisco Data turns production traces into training environments for frontier models.
We've reached $100M+ in annualized revenue run rate and raised a $30M seed round led by Khosla Ventures.
Our founders previously grew Mercor's code team from $0 to $500M in annualized revenue run rate, leading its largest code engagements and synthetics work.
Our team brings experience from Google DeepMind, Stanford SAIL, and Berkeley RISE Lab, including TerminalBench co-authorship.
We're a team of five, all technical.

Research Engineer at San Francisco Data

Research Engineer