Research Scientist, Frontier Capabilities
Core
Building agentic LLMs that learn from experience, reason effectively, and improve through interaction in scientific domains with sparse feedback and structural distribution shifts.
Role type
Senior IC research scientist (agentic systems & scientific reasoning)
Builds
Autonomous research systems, distillation strategies, and synthetic data pipelines for scientific reasoning
Domain
AI/ML, scientific discovery, agentic systems
Deliverable
production ML models
Required skills
LLMs, empirical research, large-scale training/evaluation pipelines, hypothesis design, ablation studies, long-horizon planning, memory architectures, recursive self-improvement, multi-agent coordination, continual learning, synthetic data generation, curriculum learning, reinforcement learning, program synthesis, evaluation frameworks
Preferred skills
distillation, self-improvement loops, RLHF, on-policy methods, planning/search at scale, agentic systems with tool use, coding benchmarks, long-horizon tasks
Responsibilities
Create and analyze long-running auto-research systems that propose and verify hypotheses; Design planning frameworks for agentic systems operating over long, sparse feedback loops; Develop distillation strategies from large or ensemble models into deployable systems; Design and benchmark inference-time search, sampling, and verification strategies; Propose new techniques in synthetic environment creation and curriculum learning; Measure the end-to-end impact of inference-time improvements on real scientific tasks
Seniority
Senior, hands-on IC
