Staff, ML Engineer - Scene Generation

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 is marching towards its AV 3.0 strategy. The Scene Generation team builds sensor simulation based on Neural Rendering and generative models for Torc's data-driven simulator. High-quality data from novel scenarios closes the domain data gap, solving one of the biggest challenges for safe autonomous driving. The team works on state-of-the-art approaches for Neural Rendering and data generation for camera, LiDAR, and Radar sensor data, contributing to Torc's data workflow for training and validation across the complete AV stack. As Staff ML Engineer, you will lead this team, setting its technical direction and day-to-day priorities. 

What You'll Do 

• Serve as the team's technical lead, owning key design decisions and driving consensus across engineers and stakeholders.
• Set and own the technical vision and roadmap for the Scene Generation team's Neural Rendering and generative modeling work, aligned to Torc's AV 3.0 strategy.
• Lead the Scene Generation team day to day, prioritizing work, unblocking engineers, and making the final call on architecture and technical approach.
• Implement and guide the team in applying the latest research advances in Neural Rendering and generative models.
• Translate cutting edge research into production quality Camera, LiDAR, and Radar sensor simulation that scales perception simulation and AV 3.0 training.
• Own the neural rendering framework end to end, from research, design, and implementation through testing, cloud integration, and deployment.
• Understand the integration of the framework in a cloud environment and automate the pipeline to scale target verification and validation of our autonomous trucks.
• Design, implement, test, and deploy shippable production quality software using disciplined software development processes, setting the technical bar for the team.
• Work within the cloud machine learning ecosystem alongside other machine learning services across the company, owning integration and scaling decisions for the team's pipeline.
• Proactively assess current capabilities to identify areas for improvement, proposing and driving solutions that align with core strategy and operations.
• Drive alignment across team interfaces to the rest of the organization, representing Scene Generation's roadmap, priorities, and constraints to peer teams and leadership.
• Mentor, coach, and grow engineers on the team, and play an active role in hiring, onboarding, and career development for the group. 

What You'll Need to Succeed 

• Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 10+ years of experience OR Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 7+ years of experience OR PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 5+ years of experience.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• PhD or equivalent work experience of 8+ years in a relevant field (CS, Robotics, Electrical Engineering), with industry experience shipping production software and leading technical teams.
• Proven expertise in Neural Rendering (Neural Radiance Fields and 3D Gaussian Splatting) and generative models (Diffusion Models, Flow Matching).
• Experience with production data in the context of robotics and/or autonomous driving
• Background in Computer Vision, Computer Graphics, 3D Reconstruction, or 3D Computer Vision.