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As a Research Engineer, Scaling, you will design and build infrastructure to support training, evaluation, and deployment at scale across 1X’s fleet of robots. You will take experimental and prototype systems, and transform them into production‑grade systems capable of large‑scale training runs, reliable inference, and efficient edge deployment. Your work will directly impact throughput, latency, and model performance across both datacenter and on‑device environments.
Job Responsibility:
Own and lead scaling of both distributed training and inference systems
Ensure compute resources are sufficient so that data, not hardware, is the limiter
Enable massive training at scale (1000+ GPUs) on robot data, handling fault tolerance, experiment tracking, distributed operations, and large datasets
Optimize inference throughput in datacenter contexts (e.g., for world models and diffusion engines)
Reduce latency and optimize performance for on‑device robot policies through techniques like quantization, scheduling, distillation, etc.
Requirements:
Strong programming experience in Python and/or C++
Deep intuitive understanding of what affects training or inference speed: from bottlenecks to scaling laws
A mindset aligned with extremely high scaling: belief that scale is foundational to enabling humanoid robotics
Degree in Computer Science or a related field
Hands‑on experience with distributed training frameworks (e.g., TorchTitan, DeepSpeed, FSDP/ZeRO), multi‑node debugging, experiment management
Proven skills optimizing inference performance: graph compilers, batching/scheduling, serving systems (e.g., using TensorRT or equivalents)
Familiarity with quantization strategies: PTQ, QAT, INT8/FP8
tools like TensorRT, bitsandbytes, etc.
Experience writing or tuning CUDA or Triton kernels
understanding of hardware features like vectorization, tensor cores, and memory hierarchies
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