Senior Engineer - Perception Modeling

Motional•Boston, MA

About The Position

On our Perception team, you have the opportunity to work with world-class ML engineers and research scientists, whose mission is to make self-driving vehicles a reality and to create a positive social impact. Our team works on the tech stack responsible for perceiving the dynamic scenarios, and further tracking and classifying objects around our robo-taxi. We are looking for engineers who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.

Requirements

3+ years of experience in Machine Learning, Computer Science, Robotics, or a related field
Masters or Ph.D. in Machine Learning, Computer Science, Robotics, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience
In-depth understanding of common Machine Learning and Deep Learning algorithms
Experience with designing, training, and analyzing neural networks for at least one of the following applications: object detection, semantic/instance segmentation, visual classification, motion/gesture recognition, sensor fusion, multitask learning, multi-object tracking, and/or end-to-end perception
Experience with deep learning frameworks such as TensorFlow or PyTorch
Fluency in Python, including standard scientific computing libraries and Python bindings development experience
Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods
Excellent communication and interpersonal skills
Experience mentoring and leading others

Nice To Haves

Proven track record in designing, training, or fine-tuning Foundation Models (e.g., Vision-Language Models, Multimodal Transformers) for autonomous driving or robotic perception
Deep expertise in World Models, generative video/lidar prediction, scene synthesis, and sensor simulation for closed-loop evaluation
Strong experience with Knowledge Distillation and model compression techniques to transfer capability from large offline teacher models to lightweight edge deployments
Demonstrated experience with Synthetic Data Generation (SDG) and multi-modal automated labeling systems to scale training data quality and volume
Publications in premier computer vision or machine learning conferences (e.g., CVPR, ICML, NeurIPS, ICCV, ECCV)

Responsibilities

Lead the architecture and development of advanced perception systems, focusing on, large-scale multi-modal foundation models, and robust knowledge generalization across vehicle generations
Architect and scale synthetic data generation and sensor simulation capabilities to tackle long-tail edge cases, unseen off-log scenarios, and closed-loop evaluation
Drive knowledge transfer from large teacher/foundation models to efficient onboard real-time perception models via advanced distillation and post-training techniques
Productionize and deploy solutions onto autonomous vehicle fleets
Provide technical leadership, mentorship, and guidance to senior and mid-level engineers on the team