As a Member of Technical Staff, you will design and build the architectures, evaluation loops, training systems, and orchestration layers that allow machine learning research to compound. Your work will become the foundation for neo labs applying AI to materials discovery, robotics, drug discovery, climate science, and other frontier domains.
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
You have hands-on experience training machine learning models, whether in deep learning, reinforcement learning, evolutionary search, optimization, or related areas.
You are strong at systems
You have built reliable backend, cloud, distributed, or infrastructure systems. You can reason about scalability, fault tolerance, orchestration, observability, and performance.
You have research taste
You have research experience in CS, ML, AI, or a related field. Publications at top conferences like ICLR, NeurIPS, or ICML are a plus, but we care more about your ability to reason from first principles, run good experiments, and make progress on hard problems.
You move between research and engineering
You can design experiments, write training code, debug infrastructure, and ship systems that actually work.
You are driven to explore the frontier
You 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, Rust, PyTorch, TypeScript, GPU orchestration, cloud/backend infrastructure, evaluation harnesses, experiment tracking, and the systems required to turn research into a compounding loop.