About the Company 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 marching toward its AV 3.0 strategy where end-to-end Vision-Language-Action (VLA) models perceive reason and act directly from sensor data. 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. What You’ll 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. Close the data flywheel — define dataset schemas quality metrics and validation track auto-labeling quality against model requirements route model failures back into re-labeling and retraining loops. 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. Stay current — track the latest advances in multimodal models auto-labeling and end-to-end autonomous driving and translate relevant research into production data systems. What You’ll Need to Succeed Considered highly skilled and proficient in discipline conducts complex important work under minimal supervision and with wide latitude for independent judgment. 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. 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. Required Qualifications (some combination of the following skills) 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. Model Data Curation — building targeted datasets that measurably improve downstream model performance large-scale Parquet data processing (Databricks Daft Pandas etc.). Distributed ML & data frameworks — PyTorch Lightning Ray Spark or equivalent for training and large-scale data processing. Scaled MLOps & Tooling — experiment tracking model registry MLflow / Weights & Biases and ML metrics evaluation and quality. Development Tools & Eco-System (at scale) — strong Python software development VDI and cloud-based development environments CI systems (GitHub Actions) and Docker. Bonus Points! End-to-end / VLA driving — familiarity with VLM/VLA or end-to-end driving models trajectory and action grounding or chain-of-causation / reasoning-trace datasets. Auto-labeling foundation models — experience with segmentation open-vocabulary detectors or VLM/LLM-driven data engines for annotation and verification. High-throughput model serving — vLLM SGLang or similar for batch auto-labeling and inference at scale. Semantic inference & retrieval — attribute mapping semantic search and vector databases (e.g. LanceDB) for automotive data. AV data standards & tooling — scenario-description standards such as Pegasus layers parsing robotics formats (ROS bags MCAP) and optimizing columnar storage (Parquet Arrow). Cloud development & orchestration — Terraform and AWS managed services (S3 ECS Lambda DynamoDB Step Functions Athena) AWS HyperPod / Anyscale inference orchestration. Data visualization — Foxglove FiftyOne (51) three.js OpenGL or similar for dataset inspection and accessibility. Evaluation & research — closed-loop / open-loop evaluation frameworks (e.g. NavSim-style planning metrics) publications in top-tier CV/AI/Robotics venues (CVPR/ECCV/ICCV NeurIPS/ICLR/ICML CoRL). Perks of Being a Full-time Torc’r Torc cares about our team members and we strive to provide benefits and resources to support their health work/life balance and future. Our culture is collaborative energetic and team focused. Torc offers A competitive compensation package that includes a bonus component and stock options 100% paid medical dental and vision premiums for full-time employees 401K plan with a 6% employer match Flexibility in schedule and generous paid vacation (available immediately after start date) Company-wide holiday office closures AD+D and Life Insurance Additional Information At Torc we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age veteran status or disabilities. Even if you don’t meet 100% of the qualifications listed for this opportunity we encourage you to apply. Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge skills and experience. Torc’s total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered sign-on payments relocation and other forms of compensation may be provided as part of a total compensation package in addition to a full range of medical financial and/or other benefits. Job ID R-102744 Hiring Range for Job Opening US Pay Range $177300 $212800 USD