Engineering Manager - ML Platform and Infrastructure

Responsibilities

• Grow and manage a team of world-class infrastructure and systems engineers
• 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, including architecture, scheduling, and networking
• Lead efforts to optimize training and inference performance, throughput, and cost efficiency
• Set team goals and roadmap in alignment with research and production requirements
• Partner with research and stack development teams to accelerate iteration speed
• Drive hiring, mentoring, and growth for the team

Requirements

• 3+ years of engineering management experience, ideally leading infrastructure or platform teams
• 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)
• 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

Preferred Qualifications

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

About the Company

Applied Intuition is powering the future of physical AI, creating the digital infrastructure needed to bring intelligence to every moving machine on the planet, servicing industries like automotive, defense, and robotics.