Senior Machine Learning Engineer - Camera Model
T
Torc RoboticsAutonomous Vehicles
U.S.Full-TimeSenior
Salary177300 - 212800 USD per year
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Job Details
- Experience
- 6+ years of industry experience OR Master’s degree with 3+ years OR PhD with 1+ years of experience
- Required Skills
- PythonPyTorchDeep LearningComputer Vision
Requirements
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 6+ years of industry experience
- OR Master’s degree with 3+ years of experience
- OR PhD with 1+ years of experience
- Experience developing and deploying deep learning models for computer vision or perception systems
- Strong programming skills in Python and PyTorch
- Experience writing production-quality ML code
- Experience training and evaluating models using large-scale datasets and distributed compute environments
- Solid understanding of modern deep learning architectures used in perception (e.g., CNNs, transformers, multi-task models)
- Experience debugging model behavior, analyzing performance metrics, and improving model reliability
- Ability to translate ambiguous problems into structured ML solutions and deliver independently
- Experience collaborating cross-functionally to integrate ML models into larger autonomy or robotics systems
Responsibilities
- Design, develop, and deploy deep learning models for camera-based perception (e.g., object detection, segmentation, depth estimation, scene understanding)
- Own end-to-end model development for scoped areas, from data curation and training to evaluation and deployment
- Write production-quality ML code to support scalable training, evaluation, and inference pipelines
- Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization
- Contribute to and improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking
- Partner with data teams to improve dataset quality, including labeling strategies and coverage of edge cases
- Collaborate with perception, simulation, and validation teams to evaluate and integrate models into the autonomy stack
- Improve tooling, workflows, and infrastructure to accelerate experimentation and model iteration
- Contribute to model architecture decisions and technical discussions within the team
- Mentor junior engineers on implementation, debugging, and best practices
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