Engineering Manager - ML Platform and Infrastructure

Responsibilities

• Grow and manage a team of world-class infrastructure and systems engineers to deliver a best-in-class ML platform for Physical AI
• Own the design and evolution of frameworks for orchestrating distributed training and inference jobs across thousands of GPUs
• Drive the buildout and scaling of GPU cluster infrastructure, making critical decisions on architecture, scheduling, networking, and resource management
• Lead efforts to optimize training and inference performance — including throughput, fault tolerance, GPU utilization, and cost efficiency at scale
• Set team goals and roadmap in alignment with research milestones, model development timelines, and production deployment requirements
• Partner closely with research, stack development, and infrastructure teams to understand their workflows and accelerate their iteration speed
• Drive hiring, mentoring, and growth for a high-performing, mission-driven team

Requirements

• 3+ years of engineering management experience, ideally leading infrastructure or platform teams
• Passion for building and leading high-performing teams that operate at the frontier of scale
• Deep experience with distributed systems, GPU computing, or large-scale ML infrastructure
• Direct experience building or operating large GPU clusters (1,000+ GPUs)
• Strong understanding of distributed training frameworks (e.g., PyTorch Distributed, Megatron-LM, DeepSpeed, FSDP) and job orchestration at scale
• Familiarity with GPU cluster management, high-performance networking (InfiniBand, RDMA), and resource scheduling (Slurm, Kubernetes)
• Track record of building and operating systems that run reliably at massive scale

Nice to Have

• Background in training optimization techniques such as mixed-precision training, pipeline/tensor/data parallelism, or checkpointing strategies
• Experience with inference optimization (batching, model serving, quantization, compiler-level optimizations)
• Familiarity with Physical AI domains such as autonomous driving, robotics, or simulation
• Contributions to open-source ML infrastructure projects

Compensation

Base salary range: $204,000 - $343,000 USD annually (plus equity and benefits)