ML Engineer, II - Camera Models
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.
Apply Now
Apply Now
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.
Apply Now
Apply Now