Research Engineer

Dyna Robotics; Redwood City (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Dyna Robotics is seeking a Research Engineer to architect and build end-to-end training infrastructure for a multi-cloud GPU fleet. You will ensure GPUs are utilized efficiently, runs are reproducible, and researchers can iterate with a single command.

You will own training infrastructure end-to-end, working on scalable systems for large multimodal models and real-time robot data processing, with emphasis on performance, reliability, and deployable inference.

Qualifications

• 7+ years of engineering experience in HPC or ML infrastructure.
• Deep experience with PyTorch and distributed training frameworks (DeepSpeed, Accelerate).
• Hands-on cloud GPU management (GCP/AWS) and container orchestration (Kubernetes).
• Strong understanding of distributed systems, NCCL inter-node communication, memory management.

Responsibilities

• Scale distributed training infrastructure for large-scale GPU clusters, including sharding, checkpointing, and memory optimization.
• Build a research codebase and job scheduling system to enable fast iteration and reliable retries.
• Create high-throughput data pipelines for terabytes of multimodal robot data, ensuring GPUs are never starved.
• Develop low-latency production inference pipelines with quantization and model compilation (TensorRT, Triton).
• Profile GPU utilization and memory to optimize performance across the compute fleet.

Job description

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 Research 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 robotics, no matter your discipline, we want to hear from you, even if you don't check every box.