Applied AI Researcher, Benchmarking
- Compensation
- Not disclosed
- Location
- San Francisco
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- At Distyl we’re pushing the envelope of AI utilization in enterprise. This requires creative researchers who don’t just want to drive incremental improvements on benchmarks or optimize an existing pro
- Our researchers come from many academic backgrounds but have strong research track records, operate in an AI-native way, and would be bored staying on the rails of a traditional research org.
- Experience Designing and Running Evaluations: You’ve built or maintained benchmarks, test suites, or experimental frameworks to measure model or system performance
- Statistical and Analytical Rigor: You design fair, reproducible experiments and can extract signal from noisy empirical results
- Experience Building with Models, Not Just Building Models: We develop intelligent systems using models rather than training or fine-tuning them. Ideal candidates have expertise in compound AI systems,
- Proven Track Record of Research Results: Whether you’ve published in top journals, posted amazing work on twitter, or somewhere else we want to see what you've done
- Uses AI Every Day: Before you can revolutionize someone else’s workflow, you need to revolutionize yours. You should be using tools like ChatGPT, Cursor, and Perplexity to accelerate your workflow
- Strong Programming and Data Analysis Skills: While you might not consider yourself a software engineer you need to be able to build prototypes of your ideas and then perform the experiments to prove t
- Biases Towards Showing vs Telling: Our customers want to see the power of AI today vs discuss the most elegant idea that will take 5 years to realize
Full description
About Distyl AI
Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.
We research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.
Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s.
What We Are Looking For
At Distyl we’re pushing the envelope of AI utilization in enterprise. This requires creative researchers who don’t just want to drive incremental improvements on benchmarks or optimize an existing process but instead are looking to creatively redefine how software is used.
Our researchers come from many academic backgrounds but have strong research track records, operate in an AI-native way, and would be bored staying on the rails of a traditional research org.
Key Responsibilities
- The Benchmarking team defines how progress is measured. Researchers design evaluation frameworks that capture reasoning depth, interaction quality, reliability, and operational impact. They construct benchmarks that reflect real-world complexity. Their systems become the standard by which new architectures, techniques, and releases are judged.
- Researchers in Benchmarking explore new paradigms for evaluating intelligent systems: adversarial robustness testing, longitudinal performance tracking, and human-in-the-loop assessment. They investigate how metrics shape model behavior and establish rigorous methodologies for quantifying emergent capability. Their insights drive both Distyl’s internal research priorities and industry-wide standards.
Who You Are
- Experience Designing and Running Evaluations: You’ve built or maintained benchmarks, test suites, or experimental frameworks to measure model or system performance
- Statistical and Analytical Rigor: You design fair, reproducible experiments and can extract signal from noisy empirical results
- Experience Building with Models, Not Just Building Models: We develop intelligent systems using models rather than training or fine-tuning them. Ideal candidates have expertise in compound AI systems, agentic collaboration, and associated techniques (ensembling, ReAct, graph-of-thoughts, etc.)