Senior ML Engineer - Unstructured Environments
T
Torc RoboticsAutonomous Vehicle
USFull-TimeSenior
Salary199200 - 298800 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonMachine LearningPyTorchDeep LearningComputer Vision
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or related field with 6+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry
- Master’s degree in Computer Science, Software Engineering, or related field with 3+ years of professional applied ML engineering experience in autonomous systems, robotics, or a related industry
- Scientific understanding of machine learning for 3D BEV space modeling, including the ability to apply state-of-the-art ML research and methods in production environments
- Applied expertise in terrain and surface geometry modeling, multi-camera camera calibration, and sensor projection
- Experience analyzing data distributions and addressing long-tail edge cases
- Mastery of Python
- Mastery of PyTorch
- Ability to transition research-level code to production and deployment-ready standards
Responsibilities
- Develop and Optimize Computer Vision Algorithms
- Train monocular and multimodal terrain and road surface detection models
- Detect and classify objects, obstacles, traversable surfaces and environmental conditions
- Enhance perception systems to process multi-modal sensor data (camera, LiDAR, etc.) effectively
- Utilize data science techniques to analyze model performance, data distributions, and identify corner cases
- Contribute to BEV and 3D Perception Architectures
- Design and implement deep learning models for terrain and surface inference in BEV frameworks
- Integrate BEV representations into navigation and motion planning pipelines
- Develop efficient pipelines for large-scale data processing and annotation (pseudo-labeling) of sensor data (LiDAR point clouds, image frames)
- Implement data augmentation, synthetic data generation, and domain adaptation strategies to improve model robustness
- Deploy machine learning models on edge compute platforms, ensuring real-time performance and resource efficiency
- Optimize inference pipelines for embedded and ruggedized hardware platforms
- Collaborate with robotics, software, and hardware engineers to ensure seamless integration of perception systems
- Work with technical leadership to define performance metrics and improve system reliability
- Stay current with the latest advancements in computer vision, terrain modeling, BEV models, and autonomous navigation
- Translate scientific research into production-grade machine learning solutions
- Contribute to the model development roadmap and provide strategic advice to technical leadership
- Mentor and guide junior team members to enhance their technical skills and career growth
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