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Tech Stack
Tools & technologiesPythonSQL
About the role
Key responsibilities & impact- Design end-to-end data collection and evaluation pipelines for RLVR, RLHF, SFT, red-teaming, and model evaluation workflows.
- Build, test, and iterate on AI agents that automate pipeline tasks — quality gate review, expert matching, output flagging, throughput anomaly detection.
- Define data quality standards across annotation, evaluation, and expert output review.
- Engage directly with AI researchers, TPMs, and PMs at client organizations.
- Stay current with developments in LLM post-training, evaluation methodology, and data tooling.
Requirements
What you’ll need- Proficiency in Python and SQL for data manipulation, pipeline monitoring, and quality analysis
- Working knowledge of LLM internals: RLHF/SFT training loops, how prompt structure affects output distribution, RL environment setup qualities (tool use) for agentic data collection / eval projects.
- Hands-on experience with at least one agentic or LLM workflow framework (LangChain, DSPy, AutoGen, direct tool-use via API, or equivalent)
- Demonstrated ownership of a data or ML pipeline from scoping through delivery — including quality design, not just throughput tracking
- Strong written communication: you'll write technical guidelines and rubrics that distributed expert workers follow accurately, and you'll brief senior researchers on pipeline performance
- Comfort operating with ambiguity in a fast-moving environment where model requirements shift and client priorities evolve
Benefits
Comp & perks- Offers Equity 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score
ATS Keywords
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Hard Skills & Tools
PythonSQLdata manipulationpipeline monitoringquality analysisRLHFSFTdata quality standardsdata pipeline ownershipML pipeline
Soft Skills
strong written communicationownershipoperating with ambiguity