Member Of Technical Staff

What You’ll Work On

• Autonomous R&D systems:
  Design workflows for hypothesis generation, experiment planning, model training, evaluation, debugging, and iteration.
• The hill-climbing engine:
  Build systems that search large spaces of architectures, hyperparameters, datasets, losses, and training procedures, using each result to improve the next experiment.
• Frontier AI infrastructure:
  Engineer the APIs, schedulers, queues, storage, and observability that run many experiments reliably in parallel across models, datasets, and GPUs.
• Recursive improvement loops:
  Create systems where better models produce better experiments, and better experiments produce better models.

• You have trained real ML models: hands‑on experience training models in deep learning, reinforcement learning, evolutionary search, optimization, or related areas.
• You are strong at systems: built reliable backend, cloud, distributed, or infrastructure systems; reason about scalability, fault tolerance, orchestration, observability, and performance.
• You have research taste: research experience in CS, ML, AI, or related field. Publications at top conferences are a plus, but we care more about ability to reason from first principles, run good experiments, and make progress on hard problems.
• You move between research and engineering: can design experiments, write training code, debug infrastructure, and ship systems that actually work.
• You are driven to explore the frontier: want to accelerate scientific discovery and are comfortable exploring uncharted directions with minimal supervision.

• We use whatever safely and rapidly scales the system. Today that includes Python, PyTorch/JAX, distributed training, GPU orchestration, cloud/backend infrastructure, evaluation harnesses, experiment tracking, Rust/C++, Type Script, and the systems required to turn research into a compounding loop.

San Francisco, CA
· Full‑time