Engineering Manager - ML Platform and Infrastructure

About Applied Intuition

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy.

Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.;
San Diego;
Ft. Walton Beach, Florida;
Ann Arbor, Michigan;
London;
Stuttgart;
Munich;
Stockholm;
Bangalore;
Seoul; and Tokyo. Learn more at

We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.

As an Engineering Manager on the ML Platform team, you'll lead a world-class group of engineers focused on building the infrastructure that powers Physical AI r team will own three critical areas:
Training & Inference Orchestration, where we build frameworks to efficiently schedule and run massive jobs across thousands of GPUs; GPU Cluster Architecture, where we design and scale what will be the largest GPU cluster for Physical AI in the industry; and Performance Optimization, where we push the limits of hardware utilization, throughput, and cost efficiency for large-scale training and inference workloads.

You'll work at the intersection of systems engineering and ML, partnering directly with stack development and research teams to remove bottlenecks and accelerate the path from experimentation to production.

• Grow and manage a team of world-class infrastructure and systems engineers with the goal of delivering 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 our 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

• 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, Deep Speed, FSDP) and job orchestration at scale
• Familiarity with GPU cluster management, high-performance networking (Infini Band, RDMA), and resource scheduling (Slurm, Kubernetes)
• Track record of building and operating systems that run reliably at massive scale

• 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…