Research Engineer
At a glance
Mid-level AI Research role at Dyna Robotics. Redwood City · full-time · no salary stated · we estimate $135,000–$232,000 across the United States.
Dyna Robotics has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Scale multi-cloud GPU training with sharding, checkpointing and memory tuning
- Build research code and job scheduling that support quick experiments and recover cleanly from failures
- Move large video, proprioception and 3D robot datasets through pipelines that keep GPUs supplied
- Deliver fast inference for robot control using quantization, distillation and compilation
- Find GPU, I/O and memory bottlenecks, then improve how each compute node is used
What you bring
- At least seven years in engineering, including project leadership in HPC or ML infrastructure
- Deep PyTorch and distributed-training experience, including mixed precision and gradient accumulation
- Hands-on work with cloud GPUs and Kubernetes
- A low-level grasp of distributed systems, including race conditions, memory handling and inter-node communication
Who this fits
This suits an experienced infrastructure engineer who wants training and inference systems to run on robots, and who is interested in embodied AI. The role is based onsite in Redwood City.
From the employer
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
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.
Why this is harder than typical ML infra
The data is physical: synchronized video, proprioception, and 3D signals from real robots, not clean text.
Latency is physical too: a slow model means a robot that hesitates or misses a grasp.
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
Scale distributed training across a multi-cloud GPU fleet using sharding, activation checkpointing, and memory optimization (FSDP, ZeRO).
Build a research codebase and scheduling system (Kubernetes/SLURM) built for fast iteration, automatic retries, and painless failure recovery.
Design high-throughput pipelines for terabytes of multimodal robot data so dataloaders never starve the GPUs.
Ship low-latency inference for real-time robot control using quantization, distillation, and compilation (TensorRT, Triton).
Profile GPU utilization, I/O bottlenecks, and memory fragmentation to get the most out of every node.
You'll thrive here if 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.
Have 7+ years of engineering experience, including leading projects in HPC or ML infrastructure.
Know PyTorch and distributed training deeply (DeepSpeed, Accelerate, FSDP), including mixed precision and gradient accumulation.
Have hands-on experience with cloud GPU environments (GCP/AWS) and Kubernetes.
Understand distributed systems at a low level: race conditions, memory management, NCCL and inter-node communication.
Own what you ship: you design, build, and operate systems end to end to unblock fast-moving research.
Bonus Points For
Robotics data formats (MCAP, Protobuf) or multimodal models such as VLAs.
Custom kernels (Triton), compilers, or runtime optimization.
Experience as a founding or early infrastructure hire.
Side projects involving robots, simulators, or hardware.
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.