AI Software Engineer, Edge Model Optimization & Deployment

Field

AI is transforming how robots interact with the real world. Our growing ML team in Seattle builds risk‑aware, reliable, field‑ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off‑the‑shelf, purely data‑driven methods or transformer‑only architectures, combining cutting‑edge research with real‑world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.

We are seeking an accomplished Staff AI Software Engineer - Edge Model Optimization & Deployment to drive the optimization, integration, and deployment of our ML models on real robotic platforms. In this role, you will own the edge inference stack end to end, profiling and accelerating models, improving runtime performance across latency, throughput, memory, and power, and partnering closely with perception, autonomy, and platform teams to deliver robust on‑robot behavior in the field.

You will set technical direction, raise engineering rigor, and ensure our models run efficiently and reliably on constrained hardware across diverse environments.

This is an opportunity to shape the future of robotic autonomy by translating state‑of‑the‑art ML into high‑performance, production‑grade edge deployments that operate reliably in complex, dynamic environments on real robots.

• Convert and optimize 2D/3D CNNs and Transformer‑based models (PyTorch/Tensor Flow → ONNX → Tensor
  
  RT/Triton) for real‑time inference on Jetson/Orin platforms.
• Apply model compression techniques—quantization, pruning, distillation, weight sharing—to meet strict constraints on latency, memory, bandwidth, and power.
• Develop custom Tensor
  
  RT plugins and CUDA kernels for performance‑critical components.
• Integrate optimized models into the broader robotic system using ROS nodes and interfaces.
• Build benchmarks, profile and debug end‑to‑end inference pipelines, and validate performance in real‑world robotic scenarios.
• Collaborate closely with AI researchers, robotics engineers, and hardware teams to translate cutting‑edge research into robust, deployable edge solutions.
• Ensure the reliability, robustness, and stability of deployed models operating continuously in challenging, resource‑constrained environments.

• 5+ years of professional experience developing and deploying deep learning models for edge, embedded, or real‑time systems.
• PhD in Computer Science, Robotics, Electrical or Computer Engineering, or a closely related technical field.
• Strong proficiency in PyTorch, C++, Python, and CUDA for AI/ML development and model optimization.
• Hands‑on experience with Tensor
  
  RT, ONNX, and Triton, including authoring custom plugins for Tensor
  
  RT.
• Proven experience applying model optimization techniques such as quantization, pruning, and distillation in production systems.
• Deep understanding of hardware constraints and performance tuning on Jetson / ARM platforms, GPUs, and embedded Linux systems.
• Experience integrating AI models into ROS‑based robotic systems.
• Ability to work independently while collaborating effectively in a fast‑paced, cross‑functional engineering environment.

• Experience with ROS
  2.
• Experience writing and optimizing custom CUDA kernels and low‑level GPU performance tuning.
• Familiarity with Triton, ML compilers, or compiler‑level optimizations for GPU inference.
• Experience with JAX or additional ML frameworks beyond PyTorch.
• Background deploying AI systems on real robots operating in the field, not just offline or in simulation.
• Familiarity with NVIDIA’s edge and robotics ecosystem (e.g., Isaac ROS, Deep Stream, Jet Pack).