We are a fast-scaling AI startup building large foundation models for physical systems. Our training and inference challenges require deep expertise in distributed clusters, petabyte-scale data pipelines, and low-level performance optimization.
We look for infrastructure engineers who are excited to tackle unsolved problems. If you have experience building large-scale ML infrastructure in related fields such as language and vision models, robotics, or biology, we want to hear from you.
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 are looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
Strong 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 and deployment architectures
Understanding of monitoring, logging, observability, and version control best practices for ML systems
- 3-10 years of experience building large-scale ML infrastructure for core foundation models (not fine-tuning)
- Experience training large-scale foundation models and working with large GPU infrastructures
- Background in physics, robotics, biology, or AI at the frontier of these fields, ideally at a science or physical AI company (e.g., self-driving, robotics, biology)
- Proficiency with distributed training frameworks (e.g., FSDP, DeepSpeed)
- Experience with low-level GPU performance optimization and debugging (CUDA, JAX)
- Familiarity with containerization and orchestration (Kubernetes, Docker) and cloud ML/AI services (GCP, AWS, or Azure)
- Generalist mindset with experience across the ML lifecycle, able to work closely with researchers and engineers
- Mission-driven, intentional career focus with strong problem-solving and rapid execution