Senior, ML Engineer - Road & Lane Detection

Senior, ML Engineer – Road & Lane Detection

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About the Company

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.

Meet the Team

Torc’s Model Development Organization is hiring a Senior ML engineer to develop our next generation of Road‑Lane BEV and image‑space models. Your work will span training, validation, data science, architectural design, and deployment collaboration, while mentoring junior team members.

What You’ll Do Develop and Optimize Computer Vision Algorithms

• Training monocular and multimodal Road Model Detection models.
• Comprehending objects, lanes, obstacles, and weather conditions within the driving environment.
• Enhance perception systems to process multimodal sensor data (camera, LiDAR, radar) effectively.
• Utilizing data science techniques to analyze model performance, data distributions, and identify corner cases.

Contribute to BEV Self‑Driving Architectures

• Design and implement deep learning models for Road Model inference in BEV frameworks.
• Integrate BEV representations into end‑to‑end planning and control pipelines.
• Use SD maps as priors for enhanced performance.

Data Management and Processing

• Develop efficient pipelines for large‑scale data processing and annotation (pseudo‑labeling) of sensor data.
• Implement data augmentation, synthetic data generation, and domain adaptation strategies to improve model robustness.

Model Deployment and Optimization

• Deploy machine learning models on edge devices, ensuring real‑time performance and resource efficiency.
• Optimize inference pipelines for embedded and automotive‑grade hardware platforms.

Cross‑functional Collaboration

• Collaborate with robotics, software, and hardware engineering teams to ensure seamless integration of perception systems.
• Work with product and operations teams to define performance metrics and improve system reliability.

Research and Innovation

• Stay updated with the latest advancements in computer vision, Road Lane monocular and BEV models, and autonomous driving technologies.
• Translate scientific research into production‑grade machine learning pipelines.
• Publish findings in top‑tier conferences and journals (optional but encouraged).

Leadership

• Contribute to the model development roadmap and provide strategic advice to technical leadership.
• Mentor and guide junior team members to enhance their technical skills and career growth.

What You’ll Need to Succeed

• Bachelor’s degree in Computer Science, Software Engineering, or related field with 6+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry.
• Master’s degree in Computer Science, Software Engineering, or related field with 3+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry.
• Scientific understanding of machine learning for 3D BEV space modeling, including the ability to apply state‑of‑the‑art ML research and methods in production.
• Applied understanding and hands‑on expertise in lane and road geometry concepts, multi‑camera calibration, and sensor projection.
• Experience with understanding data distributions and analyzing long‑tail distributions.
• Mastery of Python and PyTorch, with the ability to transition research level code to production and deployment ready standards.

Bonus Points

• PhD in machine learning or data science.
• Proficient in writing CUDA kernels and developing custom PyTorch operations.
• Publications at top‑tier computer vision / machine learning conferences or journals (CVPR, ICCV, JMLR, IJCV).
• Applied experience using Ray in an autonomous vehicle (AV) or related environment to scale machine learning workloads, including distributed training, large‑scale experimentation, and hyperparameter tuning across multi‑node and multi‑GPU systems.

Work Location

We are open to hiring in Torc Montreal, Quebec (Canada) or Ann Arbor, MI (U.S.) offices in a hybrid capacity. We are also…