Senior/Staff Software Engineer (Machine Learning Runtime), Motion Planning

Responsibilities

Design, implement, and optimize the production runtime pipeline for ML-based autonomous vehicle planning.
Integrate machine learning models into a scalable, safety-critical planning software stack.
Optimize end-to-end inference performance through algorithm optimization, C++ implementation, profiling, memory optimization, and efficient execution on compute-constrained embedded platforms.
Develop high-performance C++ components for feature extraction, inference, post-processing, trajectory generation, and runtime orchestration.
Design runtime validation, safety guardrails, and fallback mechanisms to ensure generated trajectories are feasible, robust, and compliant with traffic rules.
Investigate complex runtime issues by tracing failures across the planning pipeline, from feature extraction through inference to trajectory generation.
Debug difficult edge-case scenarios using logs, profiling tools, visualization, offline replay, and quantitative analysis to identify root causes and implement robust solutions.
Build tooling and infrastructure to improve debugging, observability, performance analysis, testing, and runtime monitoring.
Collaborate closely with machine learning engineers to efficiently deploy new models into production while maintaining system reliability and performance.
Drive continuous improvements in runtime architecture, scalability, reliability, and maintainability.
Ensure technical work complies with the company's Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes.

Requirements

BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related field.
4+ years of experience developing high-performance production software.
Strong C++ programming skills with experience building, optimizing, and maintaining large-scale software systems.
Experience optimizing real-time systems, including latency, throughput, memory usage, and multithreaded performance.
Experience deploying machine learning inference into production environments.
Familiarity with TensorRT, CUDA, ONNX Runtime, or similar inference acceleration frameworks.
Strong software engineering fundamentals, including software architecture, testing, and code quality.
Excellent debugging and analytical problem-solving skills, with the ability to systematically investigate complex software and system-level issues.
Experience tracing failures across multi-stage pipelines, identifying root causes, and implementing reliable long-term solutions.
Experience designing validation strategies, automated testing, runtime monitoring, and observability for production systems.
Strong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment.
Excellent communication skills and experience collaborating across machine learning and systems engineering teams.

Preferred Skills

Experience with autonomous driving planning, prediction, or robotics software.
Experience profiling CPU and GPU performance using modern profiling tools.
Experience deploying software on embedded GPU platforms.
Familiarity with PyTorch and machine learning workflows.
Experience with distributed systems, CI/CD, and cloud infrastructure.
Bonus Qualifications
Candidates who stand out typically have one or more of the following:
Deep expertise in modern C++ (C++17/20) and high-performance software engineering.
Experience optimizing production inference pipelines for low latency and high throughput.
Experience developing internal debugging, visualization, profiling, or observability tools.
Proven ability to diagnose and resolve complex production issues involving machine learning, runtime software, and system integration.
Strong systems thinking with the ability to balance correctness, performance, maintainability, scalability, and safety.
Demonstrated technical leadership and experience mentoring engineers on large production software projects.