42dot Sr. Machine Learning Engineer, Autonomy at 42dot to build production-ready L4 autonomous driving systems. Develop ML models, improve training pipelines, and collaborate with engineering teams.
Responsibilities
We're building a new autonomous driving organization to deliver production-ready L4 autonomy across trucks, buses, passenger vehicles, and robotaxis.
Our approach is ML-first, centered around end-to-end learning, foundation models, vision-language-action (VLA) models, and world models.
As one of the founding members of the autonomy organization, you'll help build the next generation of autonomous driving systems from the ground up. Rather than inheriting decisions made years ago, you'll help define and implement them.
This is a hands-on engineering role. You'll build models, write production code, analyze failures, and see your work run on real vehicles.
Founding Team Opportunity
Most autonomy programs were started years ago. The major architectural decisions and engineering culture were established long before today's engineers joined.
We're at a different stage.
The engineers who join now will help define and build our autonomy platform, influencing architecture, technical direction, engineering culture, and future roadmap.
Develop and train ML models for autonomous driving.
Contribute to end-to-end driving systems, VLA models, and world models.
Build scene understanding and driving intelligence capabilities.
Improve training pipelines, evaluation systems, and data workflows.
Qualification
Demonstrated gritBachelor'sStrong fundamentals in machine learningFoundation modelsDistributed trainingInterview Process
Required
Demonstrated grit, ownership, and problem-solving ability. We value engineers who take initiative, thrive in ambiguity, chase difficult problems, and consistently deliver results.
Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
1-4 years of experience building ML systems, or equivalent research experience.
Strong Python and PyTorch (or JAX) skills.
Strong fundamentals in machine learning, deep learning, computer vision, robotics, or related fields.
Preferred
Autonomous driving or robotics experience.
Foundation models, VLMs, VLA models, end-to-end learning, or world models.
Distributed training, simulation, or large-scale data processing.
