ML Infrastructure Engineer, Training

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.

THE ROLE

As a ML Training Infrastructure Engineer, you will architect and build the systems that turn our multi-cloud GPU fleet into a training engine our researchers love. Your charter is singular and broad: own training infrastructure end-to-end so that every GPU is busy, every run is reproducible, and every researcher's next experiment is one command away.

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.

BONUS POINTS FOR

- Experience with Robotics Data Formats (MCAP, Protobuf) or multimodal models (VLAs).

- Deep ML systems experience: custom kernels (Triton), compilers, or runtime optimization.

- Experience as a founding or early-stage infrastructure hire.

Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about the intersection of geometry and robotics, we want to hear from you—even if you don't check every box.