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ML Engineer, II - Camera Models at Torc Robotics. . Location: Remote - US, Ann Arbor, MI, Montreal, Canada, Remote - Canada. Meet the Team:.  . As a Machine Learning Engineer II – Camera Models, you will help develop and deploy machine learning models that power camera-based perception for autonomous trucks. . The Camera Models team builds and maintains core vision models that enable the autonomy stack to understand the environment, detect and localize objects, and estimate scene structure from camera data..  . .  . . Working closely with teams across perception, data, and infrastructure, you will contribute to . building robust and scalable camera-based models that support safe and reliable autonomous driving in real-world freight operations..  .  . This role focuses on developing high-performance vision models and the infrastructure needed to train, evaluate, and deploy them at scale..  .  . What You’ll Do.  . . . Develop and train deep learning models for camera-based perception, enabling the autonomy stack to detect objects, understand scenes, and estimate geometric information from visual inputs..  . . . . Implement production-quality machine learning code to support model training, evaluation, and inference for camera . perception.  systems..  . . . . Analyze model performance across diverse driving scenarios, . identify.  failure modes, and improve robustness and generalization..  . . . . Contribute to the development and optimization of large-scale training pipelines, including dataset preparation, distributed training, and experiment management..  . . . . Work closely with data teams to curate and improve training datasets derived from fleet logs, simulation, and annotation pipelines..  . . . . Collaborate with cross-functional teams across . perception. , simulation, and validation to evaluate model performance and support integration into the autonomy stack..  . . . . Improve experimentation workflows and tooling to accelerate model iteration, reproducibility, and evaluation..  . . . . Contribute to discussions on model architecture, training strategies, and . perception.  system design..  .  . . . What . You’ll.  Need to Succeed.  . . . Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience..  . . . . Experience developing machine learning or deep learning models for computer vision or perception systems..  . . . . Strong programming skills in Python and PyTorch, with experience writing production-quality ML code..  . . . . Experience training and evaluating machine learning models using large datasets and scalable compute environments..  . . . . Understanding of modern deep learning architectures used in perception systems, such as . CNNs, transformers, or multi-task learning models. ..  . . . . Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines..  . . . . Ability to collaborate with cross-functional teams to integrate ML models into larger software systems..  . . .  . Bonus Points!.  . . . Experience working in autonomous driving, robotics, or simulation-based training environments..  . . . . Experience with . multi-task learning or perception architectures.  that combine multiple visual tasks..  . . . . Experience working with . large-scale data pipelines, distributed training systems (e.g., Ray), or experiment management frameworks. ..  . . . . Familiarity with . camera calibration, geometric reasoning, or 3D perception from images. ..  . . . . Experience deploying ML models into production or real-world robotics systems..  . . .