Serve Robotics is looking for a Software Engineer, ML Performance to bridge the gap between machine learning research and real-time deployment on robotic platforms. You will enable advanced ML models to run efficiently on edge hardware like NVIDIA Jetson devices, working closely with ML researchers, embedded systems engineers, and robotics software teams.
What You'll Do
- Own the full lifecycle of ML model deployment on robots, from handoff by the ML team to full system integration.
- Convert, optimize, and integrate trained models (e.g., PyTorch/ONNX/TensorRT) for Jetson platforms using NVIDIA tools.
- Develop and optimize CUDA kernels and pipelines for low-latency, high-throughput model inference.
- Profile and benchmark existing ML workloads using tools like Nsight, nvprof, and TensorRT profiler.
- Identify and remove compute and memory bottlenecks for real-time inference.
- Design and implement strategies for quantization, pruning, and other model compression techniques suited for edge inference.
- Ensure models are robust to the resource constraints of real-time, low-power robotic systems.
- Manage memory layout, concurrency, and scheduling for optimized GPU and CPU usage on Jetson devices.
- Build benchmarking pipelines for continuous performance evaluation on hardware-in-the-loop systems.
- Collaborate with QA and systems teams to validate model behavior in field scenarios.
- Work closely with ML researchers to influence model architectures for edge deployability and provide technical guidance on feasibility.
What We're Looking For
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, or an equivalent field.
- 3+ years of experience deploying ML models on embedded or edge platforms, preferably in robotics.
- 2+ years of experience with CUDA, TensorRT, and other NVIDIA acceleration tools.
- Proficient in Python and C++, especially for performance-sensitive systems.
- Experience with NVIDIA Jetson (e.g., Xavier, Orin) and edge inference tools.
- Familiarity with model conversion workflows (e.g., PyTorch → ONNX → TensorRT).
Nice to Have
- Master’s degree in Computer Science, Robotics, Electrical Engineering, or an equivalent field.
- Experience with real-time robotics systems (e.g., ROS2, middleware, safety-critical constraints and Linux embedded systems).
- Knowledge of performance tuning under thermal, power, and memory constraints on embedded devices.
- Experience with model quantization (e.g., INT8), sparsity, and latency-aware model design.
- Contributions to open-source ML or CUDA projects.
Technical Stack
- PyTorch, ONNX, TensorRT, CUDA, NVIDIA Jetson, Python, C++
Team & Environment
You'll join an agile, diverse, and driven team. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.