Research Engineer

About the role

Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors.

Most ML infrastructure work ends at a benchmark. Ours ends at a robot doing real work at a customer site. You’ll own our training and inference infrastructure end to end, from the moment robot data lands to the moment a new model runs on hardware. The goal: every GPU busy, every run reproducible, and every researcher’s next experiment one command away.

This is a full-time, on-site position in Redwood City.

WHY THIS IS HARDER THAN TYPICAL ML INFRA

- Physical Data: The data is physical: synchronized video, proprioception, and 3D signals from real robots, not clean text.

- Physical Latency: Latency is physical too: a slow model means a robot that hesitates or misses a grasp.

- Closed Loop: The loop is closed: data from deployed robots feeds back into training, and your systems sit at the center of that flywheel.

WHAT YOU’LL DO

- Distributed Training: Scale distributed training across a multi-cloud GPU fleet using sharding, activation checkpointing, and memory optimization (FSDP, ZeRO).

- Research Codebase & Scheduling: Build a research codebase and scheduling system (Kubernetes/SLURM) built for fast iteration, automatic retries, and painless failure recovery.

- Data Pipelines: Design high-throughput pipelines for terabytes of multimodal robot data so dataloaders never starve the GPUs.

- Low-Latency Inference: Ship low-latency inference for real-time robot control using quantization, distillation, and compilation (TensorRT, Triton).

- Performance Profiling: Profile GPU utilization, I/O bottlenecks, and memory fragmentation to get the most out of every node.

WHAT YOU’LL BRING

- Passion for Embodied AI: You genuinely care about embodied AI. You follow robot learning research, have opinions about VLAs or world models, or have built something that moves. You want your work to show up in the physical world, not just on a dashboard.

- The Experience: 7+ years of engineering experience, including leading projects in HPC or ML infrastructure.

- PyTorch & Distributed Training: You know PyTorch and distributed training deeply (DeepSpeed, Accelerate, FSDP), including mixed precision and gradient accumulation.

- Cloud & Kubernetes: You have hands-on experience with cloud GPU environments (GCP/AWS) and Kubernetes.

- Distributed Systems: You understand distributed systems at a low level: race conditions, memory management, NCCL and inter-node communication.

- Ownership: You own what you ship: you design, build, and operate systems end to end to unblock fast-moving research.