Senior Machine Learning Engineer – Road & Lane Detection

Meet the Team 
 

At Torc Robotics, we're on a mission to revolutionize freight movement through safe, efficient, and reliable autonomous driving technology. Backed by Daimler Truck, we are industry leaders in Level 4 autonomous vehicle systems, with decades of innovation and a clear path to commercialization. Join our growing Model Development team and directly contribute to our world-class machine learning systems for Road & Lane detection– a critical function in enabling AV perception and path planning. 

We are seeking a highly motivated Senior Machine Learning Engineer to join our Road & Lane Detection team focused on developing robust models that predict static and semi-static road features (lanes, intersections, boundaries, driveable space, etc). You will be responsible for designing and implementing state-of-the-art deep learning models that enable our autonomous vehicles to understand and anticipate road structures in diverse environments. 

This is a hands-on applied research and development role, with direct impact on Torc’s core autonomy stack. 

What You’ll Do 

• Design, train, and deploy deep learning models for road and lane topology prediction, including drivable space, lane boundaries, and intersection structures. 

• Build and optimize neural network architectures that leverage multi-modal sensor data (camera, LiDAR, radar) and SD/HD map context. 

• Collaborate with teams across perception, mapping, planning, and systems integration to ensure seamless performance in real-world autonomous driving. 

• Lead model ablation studies, error analysis, and performance validation using large-scale simulation and real-world datasets. 

• Develop tooling and workflows to automate training, experimentation, and evaluation of ML models. 

• Mentor junior engineers and contribute to technical leadership within the ML modeling group. 

What You’ll Need to Succeed 

• Bachelor's degree in computer science, data science, artificial intelligence or related field with 6+ years of professional experience or a master's degree with 4+ years of experience 

• Hands on experience with segmentation tasks like lane prediction, free space segmentation, etc. 

• State of the Art AV experience with multi-sensor data, especially in perception systems for autonomous vehicles or robotics. 

• Mastery of Python and PyTorch, with the ability to transition research level code to production and deployment ready standards  

• Proficiency in Python, and familiarity with modern ML Ops tools and GPU-based training. 

• Prior experience in autonomous driving, robotics, or similar safety-critical domains. 

• Experience with LiDAR, radar, or 3D spatial data processing. 

• Knowledge of performance metrics for perception and prediction tasks (IoU, FDE, ADE, mAP). 

 

Bonus Points 

• PhD in machine learning or data science 

• Proficient in writing CUDA kernels and developing custom PyTorch operations. 

• Experience with relevant NVIDIA libraries and frameworks, such as CUBLAS, CuDNN, and NPP 

• Proficiency with Ray 

• Publications or contributions to open-source ML projects. 

• C++ skills or experience integrating ML into production autonomy systems.