Torc Robotics
Senior, Machine Learning Engineer - Camera Model
$177K–$213K
Remote - U.S +1 · Remote · Senior · Full-time · Posted Sep 21, 2026
- Location
- Remote - U.S
Remote - US - Experience
- Bachelor’s degree: 6+ years of industry experience; Master’s degree: 3+ years; PhD: 1+ years.
- Education
- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field; or Master’s degree or PhD.
About the job
Develops, deploys, and improves camera-based machine learning models for perception in autonomous trucks, from data curation and training through evaluation and integration into the autonomy stack.
What you'll do
- Own end-to-end development of camera perception models and production-ready training, evaluation, and inference pipelines.
- Analyze performance and failure modes across driving scenarios to improve model robustness and generalization.
- Collaborate with data, perception, simulation, and validation teams to improve datasets and integrate models.
What they ask for
- Experience developing and deploying deep learning models for computer vision or perception systems.
- Strong programming skills in Python and PyTorch, including production-quality ML code.
- Experience training and evaluating models with large-scale datasets and distributed compute environments.
- Understanding of modern perception architectures, debugging model behavior, and collaborating to integrate models into autonomy or robotics systems.
Nice to have
- Experience in autonomous driving, robotics, or simulation-based ML systems.
- Experience with Ray, multi-task perception architectures, camera calibration, geometric reasoning, or 3D perception.
Pay
$177,300 — $212,800 USD (base salary)
Tools
Python, PyTorch, Ray, CNNs, transformers, BEV, monocular depth, structure-from-motion
AI focus
Develop and deploy deep learning models for camera-based perception, including object detection, segmentation, depth estimation, and scene understanding for autonomous trucks.
Model-written summary · the original posting is authoritative