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. Who You Are 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. The Stack 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++, TypeScript, and the systems required to turn research into a compounding loop. San Francisco, CA · Full‑time #J-18808-Ljbffr Thesis (YC F25)