Compute Platform Lead
Team: Compute Platform
Location: Boston
Commitment: Full Time
Workplace Type: hybrid
Responsibilities:
- End-to-end compute architecture spanning cloud ML training, simulation clusters, data pipelines, and edge/onboard systems.
- Infrastructure that supports large-scale distributed training, high-fidelity simulation (SIL/HIL), and autonomy validation workflows.
- GPU/accelerator orchestration, workload scheduling, and performance optimization for compute-intensive autonomy systems.
- Design decisions that balance determinism, latency, safety, scalability, and cost.
- Platform reliability standards for systems that ultimately support flight-critical software.
- Long-term roadmap for scaling compute infrastructure as fleet size, simulation fidelity, and ML workloads grow.
- Technical mentorship and bar-raising across platform and infrastructure engineering.
Requirements:
- 10+ years of experience building large-scale distributed or high-performance compute systems.
- Proven ownership of production infrastructure supporting autonomous systems, robotics, aerospace, or ML-heavy platforms.
- Deep expertise in distributed systems design, networking, and systems performance optimization.
- Experience architecting infrastructure for GPU-based training, simulation, or real-time compute workloads.
- Strong programming background in C++, Go, or Python, with comfort operating close to systems layers.
- Experience bridging cloud infrastructure and edge/embedded compute environments.
- Track record of leading complex cross-functional technical initiatives.
- Ability to operate with autonomy in a fast-scaling, high-ambiguity startup environment.
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