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.
What You'll Be Doing:
- 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
What We're Looking For:
- 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
Preferred, but not required:
- 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)
The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.
Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more.
Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more.
Our journey is always people first.
We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.
Higher purpose, greater impact.
We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.
Scale up, not starting up.
Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.
Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit www.Motional.com and follow us on Twitter, LinkedIn, Instagram and YouTube.
Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibility.
Motional Boston, Massachusetts, USA Office
Boston, MA, United States
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