Staff Machine Learning Infrastructure Engineer

Join us to shape the next frontier of AI-driven robotics! Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry. Dyna Robotics was founded by repeat founders Lindon Gao and York Yang, who sold Caper AI for $350 million, and former DeepMind research scientist Jason Ma. The company has raised over $140M, backed by top investors, including CRV and First Round. We're positioned to redefine the landscape of robotic automation. Position Overview As a Lead ML Infrastructure Engineer, you are the architect of our "Training Engine." You will bridge the gap between raw hardware and cutting-edge research, ensuring that our ML team can iterate at lightning speed without friction. Your goal is simple: maximize the "intelligence-per-watt" by optimizing every millisecond of the training and inference pipeline. What You’ll Do Scale Distributed Training: Architect and own the infrastructure for large-scale GPU clusters. You’ll implement sharding, activation checkpointing, and memory optimization (ZeRO, FSDP) to enable the training of massive multimodal models. Optimize Researcher Ergonomics: Build a research codebase and job scheduling system (Kubernetes/SLURM) that prioritizes fast iteration, automated retries, and seamless failure recovery. High-Performance Data Handling: Design high-throughput pipelines to ingest and transform terabytes of multimodal robot data (video, proprioception, 3D signals), ensuring dataloaders never starve the GPUs. Production Inference: Build low-latency inference pipelines for real-time robot control. You’ll apply quantization, distillation, and model compilation (TensorRT, Triton) to move models from the lab to the physical world. Deep Systems Profiling: Dive into the weeds of GPU utilization, I/O bottlenecks, and memory fragmentation to squeeze every bit of performance out of our expanding compute fleet. What You’ll Bring 7+ Years of Engineering: With a track record of leading technical projects in high-performance computing (HPC) or ML infrastructure. ML Systems Mastery: Deep experience with PyTorch and distributed training frameworks (DeepSpeed, Accelerate). You understand the nuances of mixed precision and gradient accumulation. Infrastructure Expertise: Hands-on experience managing cloud GPU environments (GCP/AWS) and container orchestration (Kubernetes). Low-Level Intuition: A fundamental understanding of distributed systems, including race conditions, memory management, and NCCL/inter-node communication. Ownership Mindset: You don't just "deploy" code; you design, build, and operate systems end-to-end to unblock fast-moving research.