Senior, ML Engineer - Unstructured Environments

Confirmed live in the last 24 hours

Torc Robotics

Compensation

$199,200 - $298,800/year

Remote - US

Remote

Posted March 24, 2026

Job Description

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

Meet the Team:

Torc is hiring a Senior ML Engineer who develops our next-generation perception models for operating in complex, unstructured environments. Our systems utilize cutting-edge deep learning techniques to perceive the vehicle’s surroundings, detect objects and terrain features, and enable accurate autonomous navigation decisions in challenging conditions.

We are seeking a highly experienced AI/ML engineer to join our team. This is an excellent opportunity to have a significant impact on autonomous systems operating in real-world, demanding environments by leveraging advanced AI techniques.

As a Senior ML Engineer, you will apply machine learning perception techniques to build an on-vehicle perception processing pipeline, using both unimodal and multimodal models to create 3D representations of terrain surfaces and navigable areas. Training, validation, data science, inference optimization, and architectural design are your daily work. You will work closely with a group of fast-moving engineers to prototype, train and deliver scientific advances in perception to vehicles operating in challenging real-world environments.

What You'll Do:

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.
Data Management and Processing
- Develop efficient pipelines for large-scale data processing and annotation (pseudo-labeling) of sensor data (LiDAR point clouds, image frames). <