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Antioch is a simulation platform for physical AI, enabling teams to develop, evaluate, and generate synthetic data for physical systems using high-fidelity cloud simulation accelerated by 3D-native agents. The company works with leading robotics, autonomy, and perception teams to make deployment of physical autonomous systems safer, faster, and more cost-effective.
You will lead the core engineering team across infrastructure, backend, frontend, and developer experience, sitting on the leadership team to define technical direction, grow the team, and steer the engineering process. This is a hands-on role where you'll write significant code and build important platform components yourself—not a pure people-management position.
Key responsibilities include:
- Leading the core engineering team and shipping critical platform components
- Designing and scaling the GPU cloud platform, including scheduling, orchestration across GPU pools and clouds, large-scale data pipelining and storage, and complex networking
- Building infrastructure for reliable large-workload execution: distributed job execution and tracking, dataset and artifact versioning, and reproducible pipelines
- Owning cost optimization, reliability, and scalability (GPU utilization, compute spend, observability, on-call, incident response)
- Hiring excellent engineers and developing them into high-agency team members who own systems end-to-end
- Defining how engineering works at Antioch: code review, architecture decisions, releases, security, compliance, and planning
- Standardizing how the team leverages coding agents to accelerate internal engineering work
- Collaborating with founders, simulation engineers, ML engineers, technical artists, and platform engineers on roadmap, customer deployments, integrations, and escalations
You'll work in a fast-paced environment alongside a founding team with experience at Tesla Autopilot, Meta Reality Labs, and other leading companies.
REQUIREMENTS:
- 5+ years in engineering, including experience leading engineering teams at fast-growing companies
- Hands-on experience building and running GPU-accelerated systems at scale in the cloud (ML training/inference platforms, render farms, batch simulation, HPC)
- Strong background in MLOps and distributed systems: Kubernetes, distributed compute and job scheduling (Ray, Slurm, or similar), data pipelines
- Good judgment on performance and cost at scale: GPU utilization, capacity planning, compute spend across clouds
- Regular code writing with deep Python fluency; Rust, C++, or Go fluency is a plus
- Track record of hiring and developing great engineers, including hiring process design experience
- Fluent use of coding agents, agentic harnesses, and agentic DevOps tooling as a force multiplier (not a substitute for strong fundamentals)
- Clear communication with customers, executives, and engineers
NICE-TO-HAVES:
- Experience at fast-moving hardware companies (Tesla, SpaceX, Anduril, Waymo)
- Experience building GPU cloud or ML infrastructure products
- Experience with robotics, autonomy, simulation, or 3D rendering pipelines (Isaac Sim, Omniverse, Unreal)
- Experience shipping SDKs, APIs, CLIs, and technical documentation
- Experience with enterprise compliance, security, and networking (SOC 2, VPC, on-prem deployments, government/defense customers)
- Experience building MCP servers, agent tool interfaces, or LLM eval harnesses
