ML Engineer II – Birds Eye View
Torc Robotics
full-time
Posted on:
Location Type: Remote
Location: Missouri • United States
Visit company websiteExplore more
About the role
- Develop and train machine learning models for scene understanding, including tasks such as object detection, road and lane prediction, semantic voxel grid classification, occupancy prediction, and map understanding in bird’s-eye-view (BEV) space.
- Implement production-quality ML code to support model training, evaluation, and inference within the perception stack.
- Analyze model performance, identify failure modes, and propose improvements to increase robustness across diverse driving environments and conditions.
- 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.
- Contribute to multi-modal perception systems, combining signals from LiDAR, cameras, radar, and map sources into unified scene representations.
- Work with large-scale datasets from simulation, fleet logs, and on-vehicle data to curate training data and improve model performance.
- Collaborate with data, deployment, and infrastructure teams to evaluate perception models and ensure reliable performance in real-world driving scenarios.
- Help integrate perception models into the autonomy stack and testing pipelines, enabling faster experimentation and iteration.
- Contribute to tooling and infrastructure that improves training efficiency, experiment tracking, and reproducibility.
- Participate in technical discussions around model architectures, sensor fusion strategies, and training approaches within the team.
Requirements
- 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.
- Strong understanding of computer-vision, and machine learning basics.
- Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments.
- 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 ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence 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.
- Good technical communication skills, written and verbal.
- A positive team-player mindset.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningobject detectionroad predictionlane predictionsemantic voxel grid classificationoccupancy predictionPythonPyTorchimitation learningreinforcement learning
Soft Skills
technical communicationcollaborationteam playerproblem-solvinganalytical thinking
Certifications
Bachelor's degree in Computer ScienceBachelor's degree in RoboticsBachelor's degree in Electrical EngineeringBachelor's degree in Machine LearningMaster's degree in a related technical field