Position OverviewAs 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.
At
Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect.
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.
