Job Description Summary

The Model Development department is seeking a Machine Learning Engineer to contribute to building our next-generation BEV space models.

As a key 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 will collaborate closely with deployment-focused teams to ensure system reliability and actively engage with the latest research trends, aiming to translate scientific advancements into robust, production-grade machine learning pipelines.

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.

What You Will Do:

Develop and Optimize Multi-Modal Perception Models

• 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.

What You will Need to Succeed:

• Bachelor's degree in computer science, data science, artificial intelligence, or related field with 4+ years of professional experience or a master's degree with 2+ years of experience.

• Strong understanding of computer-vision, and machine learning.

• Experience with training and validating machine learning models.

• Mastery of Python and experience with PyTorch.

• Experience working with machine learning pipelines, familiarity with distributed systems such as Ray, and a strong interest in scalable training and deployment workflows.

• Good technical communication skills, written and verbal.

• A positive team-player mindset.

Bonus Points!

• PhD in machine learning or data science.

• Understanding of BEV space 3D scene modeling, and multimodal learning in autonomous systems.