Torc Robotics
Senior, ML Engineer - VLM
Ann Arbor, MI, Remote - US · Senior
Sponsorship not specifiedDetected 19 days ago
DatabricksCloud PlatformsRESTMachine LearningDeep LearningPandasComputer VisionCadenceRoboticsElectrical EngineeringSensorsResearch
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odds of building a lasting career here
42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- Meet The Team Torc is marching toward its AV 3.0 strategy, where end-to-end Vision-Language-Action (VLA) models perceive, reason, and act directly from sensor data.
Responsibilities
- Own the offline dataset pipeline - design, implement, test, and deploy Cloud-based pipelines that convert logged multi-sensor data into VLM/VLA training datasets, spanning geometric labels (3D/2D detection, tracking, segmentation, depth) through semantic, scenario-level, and action/trajectory-grounded annotations.
- Build VLM-assisted auto-labeling - develop open-vocabulary detection, dense captioning, semantic enrichment, and scene/scenario description generation that move beyond closed-set bounding boxes, using foundation models to scale annotation and cut manual labeling cost.
- Generate reasoning-grounded labels - produce language-grounded reasoning and chain-of-causation style annotations, temporally aligned to ego-motion and trajectories, to support VLA training and explainable driving behavior.
- Mine and curate the long tail - surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve downstream VLM/VLA model metrics rather than simply adding volume.
- Partner with the end-to-end model team - co-define dataset specifications with VLM/VLA model developers, own the quality bar and delivery cadence, and operationalize a continuous dataset delivery loop into their training pipelines.
- Scale on cloud infrastructure - build distributed, reproducible pipelines using columnar data formats and distributed compute, with disciplined software practices, version control, and documentation.
- Lead and mentor - serve as project lead, guide less-experienced engineers, run design reviews, set coding and annotation standards, and drive alignment across team interfaces to the rest of the organization.
- Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains, and owns team technical solutions and drives consensus. Mentors and guides engineers within the group.
- Model Data Curation - building targeted datasets that measurably improve downstream model performance; large-scale Parquet data processing (Databricks, Daft, Pandas, etc.).
- A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.
Nice to have
- Bachelor's Degree in Computer Science, Robotics, Electrical Engineering, or related technical field plus competences typically acquired through 6+ years of experience
- OR Master's Degree in a related technical field plus competences typically acquired through 3+ years of experience.
Benefits
- Computer Vision & Deep Learning - model training and at least two of: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, BEV, Depth Estimation.
- Multimodal / VLM experience - hands-on work with vision-language models, open-vocabulary or zero-shot recognition, dense captioning, or semantic embeddings / search applied to perception data.
- 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.
- Torc is marching toward its AV 3.0 strategy, where end-to-end Vision-Language-Action (VLA) models perceive, reason, and act directly from sensor data.
Company info
- At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
- Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
- Meet The Team
- High-quality, semantically rich training data is the single biggest lever for that strategy, and this team owns it.
- Sitting within Offline Perception, this team turns petabytes of logged multi-modal fleet data (images, kinematics) into VLM/VLA-ready datasets: geometric annotations, scenario-level semantic descriptions, action- and trajectory-grounded labels, and reasoning traces that explain why a maneuver was taken.
- We run a continuous data flywheel - mine long-tail and failure cases, auto-label at scale, validate quality, and feed curated datasets directly into Torc's end-to-end VLM/VLA model development.
- You will own the dataset layer that those models learn from.
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