Software Engineer, ML Performance - Remote

Overview

We are seeking a highly skilled ML Performance Engineer to join our robotics team. This technical role bridges the gap between ML research and real-time deployment, enabling advanced ML models to run efficiently on edge hardware such as NVIDIA Jetson platforms. You will work closely with ML researchers, embedded systems engineers, and robotics software teams to ensure that state-of-the-art models can be deployed with optimal performance on robotic platforms.

In Short

• 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.

Requirements

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, or equivalent field.
• 3+ years experience in deploying ML models on embedded or edge platforms (preferably 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).

Benefits

• Collaborative and respectful work environment.
• Opportunity to work on cutting-edge robotics technology.
• Engagement with a diverse and agile team.
• Contribute to solving real-world problems.