Lead Machine Learning Engineer
Job description
About the role
This role defines and delivers the production localization stack for autonomous vehicles. The position architecturally guides feature extraction and state estimation to enable precise navigation. Success directly supports safe, scalable commercial operations across diverse environments.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
Requirements
Hold a Ph.D. or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field with a strong mathematical and engineering foundation.
Bring 7+ years of industry experience developing and deploying ML/DL models for computer vision or localization at scale.
Demonstrate deep expertise in computer vision foundations, including object detection, classification, segmentation, tracking, depth estimation, 3D reconstruction, and feature detection or description.
Show proficiency in vectorized landmark and feature detection networks, BEV-based scene representation, and temporal modeling.
Have experience with self-supervised, semi-supervised learning, open-vocabulary detection, and vision/fusion foundation models.
Possess expertise in feature extraction and/or fusion from imagery, LiDAR, and/or radar.
Master ML/DL development using PyTorch or TensorFlow, including synthetic data generation, large-scale dataset handling, data curation, and active learning strategies.
Exhibit strong programming skills in Python and/or C++ with modular software design and Linux-based development experience.
Apply expertise in ML optimization for real-time products with limited compute, such as quantization and pruning of large transformer models.
Show proven leadership in developing technical roadmaps, mentoring engineers, and driving measurable improvements in model performance and system reliability.
Nice to have
Accumulate 10+ years of experience in ML/DL for autonomous driving or ADAS systems.
Apply Vision-Language Models and foundation models for auto-labeling and long-tail edge-case detection.
Use working knowledge of localization and state estimation concepts, such as SLAM and sensor fusion.
Maintain a proven record of inventions and publications at top-tier conferences.
Practical notes
The role requires standard office working conditions, including prolonged sitting, standing, and computer use.
Moderate travel is required, estimated at 11%-25%.
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.