Head of Engineering; GPU Infrastructure​/HPC)

Greylock-backed company in New York building a cloud simulation platform for robotics, autonomy, and physical AI.

The platform helps engineering teams develop and evaluate autonomous systems, generate synthetic data, and test thousands of scenarios before deploying changes to physical hardware. It combines calibrated digital twins, realistic environments, and AI agents to reduce dependence on expensive, slow, and incomplete real-world testing.

We’re looking for a highly technical engineering leader to own the core platform powering GPU-accelerated simulation at scale.

You’ll lead engineering across cloud infrastructure, backend, frontend, and developer experience. As a member of the leadership team, you’ll set technical direction, grow the team, and shape how engineering operates. You’ll also write substantial production code and build critical systems yourself.

The technical challenge spans GPU scheduling, distributed workload execution, large-scale data pipelines, storage, and networking. You’ll make these systems reliable and cost-efficient as usage grows, while preserving compatibility for customers who depend on the platform.

Strong candidates may come from ML training or inference infrastructure, GPU cloud platforms, high-performance computing, render farms, or large-scale simulation systems. Direct robotics experience is helpful, but hands-on experience operating GPU systems at scale is central to the role.

This role is based in New York City and requires working in person. Candidates willing to relocate are welcome.

• Technical direction and delivery across the core platform, including personally implementing critical components
• GPU scheduling and orchestration across compute pools and cloud providers
• Distributed job execution, workload tracking, data pipelines, storage, and reproducible workflows
• Dataset and artifact versioning for simulation, ML, and customer workloads
• Reliability, observability, incident response, capacity planning, GPU utilization, and compute costs
• Hiring and developing engineers who can own systems end to end
• Engineering practices across architecture, code review, releases, planning, security, and compliance
• Adoption of coding agents and agentic development tools across the engineering team

You’ll work closely with the founders, simulation and ML teams, and go-to-market leaders on product priorities. You’ll also engage directly with customers on deployments, integrations, and technical escalations.

You likely have experience:

• Building software for at least five years, including leading engineering teams at fast-growing companies
• Building and operating GPU-accelerated cloud systems at scale
• Working deeply with distributed systems, MLOps, Kubernetes, data pipelines, and compute scheduling tools such as Ray or Slurm
• Making practical performance and cost decisions across GPU utilization, capacity, and cloud infrastructure
• Writing production code regularly, with deep fluency in Python
• Hiring and developing strong engineers and improving the hiring process
• Using coding agents and agentic tooling with the engineering judgment to review, debug, and validate their output
• Communicating clearly with engineers, customers, and company leadership

Experience with Rust, C++, or Go is a plus.

Other useful backgrounds include robotics or simulation, developer tools such as SDKs and CLIs, enterprise security and deployment requirements, and agent infrastructure such as MCP servers or evaluation harnesses.

We’re less interested in titles than in technical credibility, ownership, and the ability to lead through direct involvement in the work.

The environment favors urgency, initiative, and…