# Engineering Manager - ML Platform and Infrastructure

**Company:** [Applied Intuition](https://hotfix.jobs/companies/applied-intuition)
**Location:** Sunnyvale, CA
**Role:** Engineering Management
**Salary:** $204k – $343k/yr
**Skills:** Pytorch Distributed, Megatron-Lm, Deepspeed, Fsdp, Kubernetes, Slurm, InfiniBand, Rdma, Gpu Computing, Distributed Systems
**Posted:** 2026-04-21

> Leads engineering team building ML platform infrastructure for Physical AI, owning GPU cluster scaling, distributed training/inference orchestration, and performance optimization. Requires 3+ years management experience with large-scale GPU systems.

## Job Description

## 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)

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