Lead Applied Scientist
Core
Design and prototype AI/ML methods to improve data quality, scale human judgement, and support robust AI evaluation workflows for frontier AI labs.
Role type
Lead Applied Scientist (Human Data Infrastructure)
Builds
Prototypes, benchmarks, and methodologies for human-in-the-loop AI systems
Domain
AI/ML, Human-in-the-loop systems, Data Quality, AI Evaluation
Deliverable
production ML models
Required skills
Applied ML, Statistics, LLMs, Agentic techniques, Python, Experiment design, Human judgement aggregation
Preferred skills
RLHF, Annotation pipelines, Data quality modelling, RAG, LLM-as-judge, Multi-agent workflows, Synthetic data generation
Technologies
Python, LLMs, RAG, Multi-agent workflows
Responsibilities
Prototype AI/ML methods to improve human data quality and evaluation workflows; Design experiments and benchmarks to measure method effectiveness; Apply classical ML, statistics, and agentic techniques for practical value; Partner with product and engineering to translate scientific methods into scalable capabilities; Communicate technical assumptions and recommendations across teams.
Seniority
Senior, hands-on IC
