Senior Machine Learning Engineer, BEV Scene Modeling

Torc Robotics; Montreal; On-site; CAD 100,000 - 130,000; Full time

Job summary

A leading company in autonomous vehicle technology is seeking a Senior Machine Learning Engineer to develop advanced models for BEV scene modeling. Candidates should have substantial experience in machine learning, a deep understanding of 3D modeling, and proficiency with Python. Join a dynamic team and contribute to the future of autonomous vehicles and freight transformation.

Qualifications

• 6+ years of professional experience or a master's degree with 4+ years of experience.
• Expertise in multimodal learning in autonomous systems.
• Track record of successfully building and shipping products containing ML models.

Responsibilities

• Design and implement deep learning models for object detection and semantic segmentation.
• Enhance perception systems for multi-modal sensor data.
• Collaborate with robotics, software, and hardware engineering teams.

Skills

Deep understanding of BEV space 3D scene modeling

Mastery of Python

Strong technical communication skills

Strong problem-solving skills

Education

Bachelor's degree in computer science, data science, artificial intelligence, or related field

Master's degree is a plus

Tools

PyTorch

Ray

Job description

Senior Machine Learning Engineer, BEV Scene Modeling

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 transformhow 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 a highly experienced senior machine learning engineer to join our scene modeling team. This is an exceptional opportunity for you to have a significant impact on the future of the autonomous vehicle industry by leveraging AI.

The Model Development department is seeking a Senior Machine Learning Engineer to help build our next-generation BEV space models.

As a senior member of the team, you will apply machine learning techniques in a production-focused environment. You’ll work with both single-modal and multimodal models to generate 3D representations of complex driving scenarios. Your daily responsibilities include model training, validation, data analysis, and architectural design. You take an active interest in how models perform in real-world deployment and collaborate closely with deployment-focused teams to ensure system reliability. You also mentor junior team members and stay up to date with the latest research trends, with a strong motivation to translate scientific advancements into robust, production-grade machine learning pipelines.

What You Will Do

Develop and Optimize Multi-Modal Perception Models

• Design and implement deep learning models for object detection, semantic segmentation, and voxel grid occupancy in BEV frameworks.
• Enhance perception systems to process multi-modal sensor data (camera, LiDAR, radar) effectively.
• Implement monocular and stereo depth estimation algorithms.
• Identify and interpret objects, lanes, obstacles, and weather conditions in the driving environment.
• Apply data science techniques to analyze model performance, understand data distributions, and identify corner cases.
• Integrate BEV representations into end-to-end planning and control pipelines.

Model Conversion, Deployment and Target Hardware Optimization

• Development of model-specific conversion, deployment and integration pipelines.
• Support the deployment of 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

• 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 will 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.
• Deep understanding of BEV space 3D scene modeling, computer vision, and multimodal learning in autonomous systems.
• Mastery of Python and expertise in one or more machine learning frameworks (e.g., PyTorch, Ray), with a strong ability to translate research-grade code into robust, maintainable, and production-ready systems.
• Track recordof successfully building and shipping products containing ML models
• Strong technical communication skills, written and verbal, that scale to a diverse workforce.
• Strong problem-solving skills and the ability to analyze and debug complex software defects.

Bonus Points!

• PhD in machine learning or data science.
• Mastery of Ray.

At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.

Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply.

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