Senior/Staff Machine Learning Engineer, Motion Planning

PlusAI•Santa Clara, CA

10d•$130,000 - $220,000

About The Position

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. We are looking for a machine learning engineer to train and deploy the latest generation of ML-based planning algorithms on the extensive data we collect every day across our autonomous trucking fleet.

Requirements

BS, MS, or PhD in Computer Science, Robotics, Machine Learning, or a related field.
4+ years of experience developing machine learning systems for robotics, autonomous driving, or other real-time decision-making systems.
Strong Python programming skills and experience with modern deep learning frameworks such as PyTorch.
Strong understanding of deep learning, sequence modeling, transformers, diffusion models, or other modern ML architectures.
Experience designing datasets, experiments, validation methodologies, and metric-driven model evaluation.
Strong software engineering skills with experience developing and maintaining production-quality software.
Excellent debugging and analytical problem-solving skills, with the ability to investigate complex issues across datasets, model behavior, and production systems.
Experience analyzing edge cases, tracing failures to their root cause, and improving model robustness through systematic experimentation.
Experience designing validation methodologies, automated testing, and monitoring to ensure correctness, safety, and production reliability.
Strong ownership mindset with the ability to drive problems from investigation through implementation, validation, and deployment.
Excellent communication skills and experience collaborating across cross-functional engineering teams.

Nice To Haves

Experience with planning, prediction, motion forecasting, or trajectory generation.
Experience deploying machine learning models into production environments.
Working knowledge of modern C++ and production software development.
Experience with TensorRT, ONNX Runtime, CUDA, or ML inference optimization.
Experience with large-scale distributed training or cloud-based ML infrastructure.
Publications or open-source contributions in machine learning, robotics, or autonomous driving.
Deep expertise in autonomous vehicle planning or motion prediction.
Experience developing production ML systems for safety-critical applications.
Experience leading technical direction for large ML projects.
Strong intuition for balancing model quality, robustness, latency, and deployment constraints.
Demonstrated ability to solve ambiguous, cross-functional engineering problems involving machine learning, software systems, and autonomous driving.

Responsibilities

Develop state-of-the-art machine learning models for autonomous vehicle planning using rich map, perception, routing, and contextual sensor data.
Design and implement model architectures for trajectory generation, behavior planning, and decision making that balance accuracy, robustness, interpretability, and runtime efficiency.
Own the end-to-end machine learning lifecycle, including data curation, feature engineering, experimentation, training, evaluation, deployment, monitoring, and continuous improvement.
Design rigorous offline evaluation methodologies, validation pipelines, and metrics to measure planning quality, safety, robustness, and generalization.
Analyze model behavior, investigate failure cases, and improve performance through systematic error analysis and targeted experimentation.
Collaborate closely with runtime, perception, prediction, mapping, and systems teams to deploy scalable machine learning solutions into production.
Design validation strategies and rule-based guardrails to ensure generated trajectories are feasible, safe, and compliant with traffic rules.
Stay current with advances in machine learning, robotics, and autonomous driving, translating research innovations into production systems.
Ensure technical work complies with the company's Quality Management System (QMS), customer requirements, regulatory standards, and internal engineering processes.