Ai Engineer • Mid-Level

Who you are

You have strong hands-on experience with deep learning models specifically tailored for autonomous driving, robotics, or sequential decision-making systems — your expertise allows you to navigate complex challenges in these fields effectively. You are familiar with applying imitation learning techniques to enhance driving behavior, demonstrating your ability to leverage advanced methodologies for practical applications.

Your practical experience extends to reinforcement learning and generative modeling, which you have utilized to improve long-horizon decision-making in autonomous systems. You understand the intricacies of multimodal models and are adept at fine-tuning and adapting VLA-style architectures for specific autonomous driving tasks using large-scale driving datasets.

You possess a solid foundation in defining and evaluating multimodal representations for driving data, including video, BEV, map, vehicle state, actions, and optional language annotations. Your analytical skills enable you to build and maintain end-to-end machine learning pipelines, ensuring that data curation, training, evaluation, and deployment processes are seamless and efficient.

Collaboration is key in your work — you thrive in environments where you can partner with perception, prediction, planning, control, and platform teams to integrate machine learning models into production vehicle software stacks. Your ability to communicate effectively with cross-functional teams enhances the overall project outcomes.

Desirable

Experience with safety and robustness evaluations in open-loop, closed-loop simulation, and real-vehicle environments is a plus. You are also encouraged to apply even if your experience doesn't match every requirement, as your curiosity and willingness to learn are valued.

What you'll do

In this role, you will design and develop VLA-based models for autonomous driving, focusing on critical aspects such as planning, decision-making, and action generation. You will apply your knowledge of imitation learning, reinforcement learning, and generative modeling to enhance driving behavior and facilitate long-horizon decision-making.

Your responsibilities will include fine-tuning and adapting multimodal models for autonomous driving tasks, ensuring that they are optimized for performance using large-scale driving datasets. You will define and evaluate multimodal representations for driving data, which will involve working with various data types including video, BEV, and vehicle state.

You will build and maintain end-to-end machine learning pipelines, overseeing the entire process from data curation and training to evaluation and deployment. Evaluating models in different environments will be a crucial part of your role, as you will focus on ensuring safety and robustness in real-world applications.

Collaboration will be a significant aspect of your daily activities, as you will work closely with teams focused on perception, prediction, planning, and control. Your contributions will help integrate machine learning models into production vehicle software stacks, enhancing the overall functionality and reliability of autonomous driving systems.

What we offer

At 42dot, you will be part of a dynamic team dedicated to advancing autonomous driving technology. We provide a collaborative work environment where innovation is encouraged, and your contributions will directly impact the future of driving. We value diversity and are committed to creating an inclusive workplace where everyone can thrive.

We offer competitive compensation and benefits, along with opportunities for professional growth and development. Join us in our mission to revolutionize the driving experience through cutting-edge technology and robust machine learning solutions.