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About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.

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.

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

Meet the team

Torc's Autonomy Applications software utilizes cutting-edge deep learning techniques to perceive the vehicle's environment, predict the movements of other vehicles, and execute accurate driving decisions. We are actively seeking highly experienced senior machine learning engineers to join our model development department. This is an exceptional opportunity for you to have a significant impact on the future of the autonomous vehicle industry by leveraging AI. 

What you'll do

Develop and optimize computer vision, lidar and radar-based ML models 

• Implementing monocular and stereo depth estimation algorithms. 

• Comprehending objects, lanes, obstacles, and weather conditions within the driving environment. 

• Enhance perception systems to process multi-modal sensor data 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 object detection, semantic segmentation, and voxel grid occupancy in BEV frameworks. 

• Integrate BEV representations into end-to-end planning and control pipelines. 

• Establish BEV level tracking capabilities 

Contribute to behavior level ML models 

• Design and implement deep learning models for learned actor prediction 

• Fuse object tracking and actor prediction models 

• Design and implement learned trajectory planning models 

Data Management and Processing 

• Develop efficient pipelines for large-scale data processing and annotation(pseudo-labeling) of sensor data (e.g., LiDAR point clouds, image frames). 

• Implement data augmentation, synthetic data generation, and domain adaptation strategies to improve model robustness. 

Model Deployment and Optimization 

• Collaborating with conversion and deployment teams to ensure seamless integration. 

• 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. 

Leadership 

• Contributing to the model development roadmap and providing strategic advice to technical leadership. 

• Mentoring and guiding junior team members to enhance their technical skills and career growth. 

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
  • Scientific understanding of machine learning for at least one of the following  
  • 3D BEV space modeling 
  • Actor prediction 
  • Learned trajectory planning 
  • Autonomous driving end to end modeling 
  • Vision, lidar and radar-based object detection and classification 
  • Multimodal multitask machine learning 
  • Pixel – level processing tasks like depth estimation, segmentation, etc. 
  • Calibration, odometry, localization 
  • Tracking 
  • 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
• 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.
• Experience with relevant NVIDIA libraries and frameworks, such as CUBLAS, CuDNN, and NPP
• Proficiency with Ray