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
Applied Scientist (AI/ML methods & evaluation)
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, Data quality modelling, Judgement aggregation
Preferred skills
RLHF, Annotation pipelines, RAG, LLM-as-judge, Multi-agent workflows, Synthetic data generation
Technologies
Python, LLMs, RAG, Agentic frameworks
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; Translate ambiguous problems into clear hypotheses, experiments, and reusable methodologies.
Seniority
Mid-Senior, hands-on IC
