Software Engineer II, MLOps Framework

As a Software Engineer on the ML Ops Framework & Conversion team, you will own the pipelines that take models from research to production on edge hardware, including model conversion, compilation, benchmarking, and release. The ML Ops Framework & Conversion team is responsible for the full model conversion process — from architecting TensorRT pipelines to maintaining the model release registry across platforms.

Responsibilities

Architect and implement model conversion and compilation pipelines using tools such as ONNX, TensorRT, and torch.compile for deployment on edge devices (e.g., NVIDIA Orin); Maintain and evolve the model release registry, ensuring traceability and reproducibility across model versions and target platforms; Perform rigorous latency benchmarking and model quality parity evaluations to validate that deployed models meet safety-critical performance requirements; Compare metrics across platforms to verify accuracy and latency compliance before release; Communicate and collaborate with model development teams and broader stakeholders, ensuring your findings translate into reliable, actionable outcomes across the organization

Requirements

Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 4+ years of experience; Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 0-3+ years of experience; Extensive experience with model conversion and compilation pipelines (ONNX, TensorRT, torch.compile) and performing rigorous latency benchmarking and quality parity validation; Hands-on experience deploying and testing models on edge hardware (e.g., NVIDIA Orin or similar embedded platforms); Experience maintaining a model release registry in a production environment; Ability to compare and interpret performance metrics across hardware platforms to validate models against strict latency and accuracy requirements

Nice to have

Expertise in model quantization (PTQ, QAT) and mixed-precision inference (INT8, FP8, FP4, BF16/FP16); Experience releasing multi-target models across heterogeneous platforms; Familiarity with SOTA autonomous driving perception algorithms — temporal 3D object detection, BEV, 3D Occupancy Networks — and multi-modal sensor fusion (vision, LiDAR, radar); C++ and/or CUDA kernel development

Benefits

A competitive compensation package that includes a bonus component and stock options; 100% paid medical, dental, and vision premiums for full-time employees; 401K plan with a 6% employer match; Flexibility in schedule and generous paid vacation (available immediately after start date); AD+D and Life Insurance