Causal Labs — Machine Learning Infrastructure Engineer

• →Design, deploy, and maintain large distributed ML training and inference clusters
• →Build efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and training across the full ML lifecycle
• →Research and test training approaches, including parallelization techniques and numerical-precision trade-offs across model scales
• →Profile and debug low-level GPU operations to optimize performance
• →Track new research and bring fresh ideas into the work

Tech stack: Distributed training frameworks (FSDP, DeepSpeed), NVIDIA GPUs, Linux, Python, C++, Kubernetes/Docker, and a major cloud platform (GCP, AWS, or Azure).

• 2–10 years building large-scale ML infrastructure for core foundation models
• Hands-on experience building infrastructure for foundation models trained from scratch, rather than fine-tuning existing models
• A background at a science-focused or physical-AI company (for example self-driving, robotics, or biology)
• Deep, demonstrable expertise optimizing large-scale training and inference workloads
• Working proficiency with distributed training frameworks such as FSDP or DeepSpeed
• A clear pattern of intentional, mission-driven career decisions
• Able to work on-site 5 days/week in San Francisco (relocation supported)

• Generalist experience spanning the full ML lifecycle
• Low-level GPU performance optimization and debugging (CUDA, JAX)

• Location: San Francisco, CA
• Work policy: In-person 5 days/week (relocation supported)
• Compensation: $200K–$400K + competitive early-stage equity
• Visa sponsorship: Open to supporting work authorization for the right candidate
• Employment type: Full-time