ML Systems Engineer, Infrastructure & Cloud

Have demonstrated expertise in ML systems engineering . Examples include: Managing distributed training jobs across hundreds of GPUs Debugging and fixing numerical instabilities in large-scale training Building infrastructure for reproducible ML experiments Optimizing training throughput and resource utilization

Managing distributed training jobs across hundreds of GPUs

Debugging and fixing numerical instabilities in large-scale training

Building infrastructure for reproducible ML experiments

Optimizing training throughput and resource utilization

Possess deep knowledge of distributed training frameworks including PyTorch/JAX distributed strategies (DDP, FSDP, ZeRO), gradient accumulation, mixed precision training, and checkpoint/recovery systems.

Have strong cloud administration skills including AWS/GCP/Azure services, infrastructure as code (Terraform), Kubernetes orchestration, cost optimization, security best practices, and compliance requirements.

Understand the full ML stack from hardware (GPUs, interconnects, storage) through frameworks (PyTorch, JAX) to high-level training loops and evaluation pipelines.

Be skilled at debugging complex failures across the stack—GPU/NCCL issues, data loading bottlenecks, memory leaks, gradient explosions, and convergence problems.

Value documentation and knowledge sharing . You maintain comprehensive logs of issues encountered, solutions found, and lessons learned, building institutional knowledge.

Progress with autonomy while coordinating closely with researchers. You can anticipate infrastructure needs, prevent problems before they occur, and respond quickly when issues arise.

Experience at organizations training large models (OpenAI, Anthropic, Google, Meta).

Background in both ML research and production systems.

Contributions to ML frameworks or distributed training libraries.

Experience with on-premise GPU cluster management.

Knowledge of optimization theory and numerical methods.

Understanding of robotics-specific infrastructure requirements.

Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.

FT/PT: Full-time.

In-person Policy: We are in the office four days a week. Be prepared to attend multi-day Basis-wide in-person events.

Location: New York City or Cambridge, MA.

Salary range: Competitive salary.

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