Example Company
Software engineer
- Location
- San Francisco
- Type
- Full time
- Compensation
- $150k-$200k
- Posted
- 2 days ago
Join a team building useful software. Work closely with engineering and design to turn new ideas into products.
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Intern
- Location
- San Francisco
- Type
- Intern
- Posted
- 6H AGO
About the role
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 small team of highly technical engineers working out of our SF office 5 days a week.
What You'll Do
Staffed on the same team as our full time engineers and researchers, working on developing new training environments and task formats for frontier models using production agent traces.
Analyze agent behavior and failure cases, and run experiments on new benchmarks and task formats
Develop tools to generate, test, and evaluate training tasks, helping improve the quality and reliability of our environments.
What We're Looking For
Prior research experience, open-source contributions, or work on agent benchmarks and ML pipelines are a plus.
Hands-on experience building AI agents, reinforcement learning environments, synthetic data pipelines, or evaluation benchmarks, with a strong understanding of the challenges involved in training and evaluating frontier models.
Proven ability to independently tackle complex technical problems, build robust systems, and design rigorous experiments to evaluate performance and drive improvements.
Curiosity about how AI systems learn and fail, and an interest in building more effective ways to train and evaluate them.
We care more about what you've built, how you think, and your willingness to learn than specific credentials or publications.
