<p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 2">About the Company</span></span></strong><span data-ccp-props="{"201341983":0,"335559738":220,"335559739":100,"335559740":240}"> </span></p> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559739":120}"> </span></p> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559739":120}"> </span></p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 2">Meet The Team</span></span></strong><span data-ccp-props="{"201341983":0,"335559738":220,"335559739":100,"335559740":240}"> </span></p> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559739":120}"> </span></p> <p><span data-contrast="none">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.</span><span data-ccp-props="{"335559739":120}"> </span></p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 2">What </span><span data-ccp-parastyle="heading 2">You’ll</span><span data-ccp-parastyle="heading 2"> Do</span></span></strong><span data-ccp-props="{"201341983":0,"335559738":220,"335559739":100,"335559740":240}"> </span></p> <ul> <li data-leveltext="•" data-font="" data-listid="4" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1"><strong><span data-contrast="none">Own the offline dataset pipeline</span></strong><span data-contrast="none"> — 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.</span> </li> <li data-leveltext="•" data-font="" data-listid="4" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1"><strong><span data-contrast="none">Build VLM-assisted auto-labeling</span></strong><span data-contrast="none"> — 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.</span> </li> <li data-leveltext="•" data-font="" data-listid="4" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1"><strong><span data-contrast="none">Generate reasoning-grounded labels</span></strong><span data-contrast="none"> — 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.</span> </li> <li data-leveltext="•" data-font="" data-listid="4" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1"><strong><span data-contrast="none">Mine and curate the long tail</span></strong><span data-contrast="none"> — surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve downstream VLM/VLA model metrics rather than simply adding volume.</span> </li> <li data-leveltext="•" data-font="" data-listid="4" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1"><strong><span data-contrast="none">Close the data flywheel</span></strong><span data-contrast="none"> — define dataset schemas, quality metrics, and validation; track auto-labeling quality against model requirements; route model failures ba