Machine Learning - Infrastructure
Causal LabsSan Francisco, CA, United StatesHace 1 día
Tipo de contrato
- A tiempo completo
Descripción del trabajoStrong grasp of state‑of‑the‑art techniques for optimizing training and inference workloads Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML / AI service offerings Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker) Background working on distributed task management systems and scalable model serving & deployment architectures Understanding of monitoring, logging, observability, and version control best practices for ML systems Work on deeply challenging, unsolved problems Competitive cash and equity compensation Medical, dental, and vision insurance Catered lunch & dinner Unlimited paid time off Visa sponsorship & relocation support
About us
Our mission is to build causal intelligence, starting with physics models to predict and control the weather.
We're building a small team driven by a deep passion and urgency to solve this civilizationally important problem.
Our founding team has led & shipped models across self‑driving cars, humanoid robotics, protein folding, and video generation at world‑class institutions including Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple.
Responsibilities
- Design, deploy, and maintain large distributed ML training and inference clusters
- Develop efficient, scalable end‑to‑end pipelines to manage petabyte‑scale datasets and model training throughout the entire ML lifecycle
- Research and test various training approaches including parallelization techniques and numerical precision trade‑offs across different model scales
- Analyze, profile and debug low‑level GPU operations to optimize performance
- Stay up‑to‑date on research to bring new ideas to work
What we’re looking for
We value a relentless approach to problem‑solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
You don’t have to meet every single requirement above.
Benefits
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Machine Learning • San Francisco, CA, United States