Senior, ML Engineer (Road & Lane Detection)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior, ML Engineer - Road & Lane Detection in Texas.

In this role, you will be at the forefront of autonomous vehicle perception, developing machine learning models that interpret and predict road and lane structures in complex environments. You will work with multi-modal sensor data, including cameras, LiDAR, and radar, to create robust 3D representations of driving surfaces. Your contributions will directly influence vehicle navigation and safety, optimizing algorithms for real-world deployment. You will collaborate across software, robotics, and hardware teams, guiding junior engineers and integrating cutting-edge research into production-grade ML pipelines. This role is ideal for someone passionate about AI, computer vision, and autonomous driving, seeking a fast-paced environment with tangible impact on the future of mobility.

• Develop and optimize computer vision algorithms for road and lane detection in monocular and multi-modal contexts
• Design and implement deep learning models for BEV (bird’s eye view) representations integrated into planning and control pipelines
• Analyze model performance, data distributions, and edge cases using advanced data science techniques
• Build efficient pipelines for large-scale data processing, annotation, augmentation, and domain adaptation
• Deploy and optimize ML models for real-time inference on automotive-grade hardware
• Collaborate cross-functionally with robotics, software, hardware, product, and operations teams to ensure seamless system integration
• Mentor junior engineers and contribute to the technical roadmap, staying current with the latest advancements in ML and computer vision

• Bachelor’s degree with 6+ years or Master’s degree with 3+ years of professional experience in Machine Learning Engineering, Autonomous Vehicles, Robotics, or a related field
• Strong expertise in 3D BEV space modeling, lane and road geometry, multi-camera calibration, and sensor projection
• Proficiency in Python and PyTorch, with experience translating research code into production-ready systems
• Hands-on experience with multi-modal sensor data (camera, LiDAR, radar) and data management pipelines
• Experience deploying and optimizing ML models for embedded and real-time systems
• Strong analytical, problem-solving, and communication skills, with a track record of mentoring or leading technical teams
• Preferred: PhD in ML or Data Science, CUDA programming, custom PyTorch operations, publications in top-tier ML/Computer Vision conferences, experience with distributed training and multi-GPU systems

• Competitive salary ($199,200–$298,800) plus bonus and stock options
• 100% paid medical, dental, and vision coverage
• 401(k) plan with 6% employer match
• Flexible schedule and generous paid vacation, plus company-wide holiday closures
• Life and AD&D insurance
• Collaborative, energetic, and team-focused work environment
• Opportunities for professional growth and mentorship in cutting-edge ML and autonomous vehicle technologies
• Hybrid or remote work options available in the United States

Why Apply Through Jobgether?

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

 Why Apply Through Jobgether? 

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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