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AI Summary
Architect and scale distributed training infrastructure for large-scale multimodal robotic AI models. Build high-throughput data pipelines and low-latency inference systems to support real-time robot control. Requires 5+ years of experience in ML infrastructure, PyTorch, and distributed training frameworks.
Key Highlights
Own end-to-end training infrastructure for a robotics company with $140M+ funding.
Work on-site in Redwood City, CA, with visa sponsorship available.
Competitive base salary of $220K–$350K plus equity.
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Key Responsibilities
Architect and scale distributed training across large GPU clusters using sharding, activation checkpointing, and memory optimization.
Build researcher-friendly tooling and job scheduling on Kubernetes and SLURM with automated retries and failure recovery.
Design high-throughput data pipelines to ingest and transform terabytes of multimodal robot data.
Build low-latency inference pipelines for real-time robot control using quantization, distillation, and model compilation.
Profile deep into the stack to optimize GPU utilization, I/O bottlenecks, and memory fragmentation.
Technical Skills Required
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Benefits & Perks
Competitive equity
Visa sponsorship (OPT and H1B transfers)
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Nice to Have
Robotics experience
Experience building multimodal systems for video, audio, or other rich media models
Job Description
ML Infrastructure Engineer | Redwood City, CA (Onsite) | $220K–$350K base
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