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Develop and evaluate methods that turn multi-sensor recordings into accurate 3D object labels offline, using models and compute the vehicle cannot afford in real time, and measure them against reviewed ground truth
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Raise label quality and reduce the human review it needs: find where machine labels fail and fix the causes in models, sensor fusion or data; bring state-of-the-art perception and foundation models into offline use and adapt them to our vehicles and sensors
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Build and operate the labeling pipelines at fleet scale: batch GPU inference, data extraction and alignment, selection of what goes to human review, annotation-tool integration, monitoring, throughput and cost, and regression tests that catch quality drops before data reaches training
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Close the loop with the modeling teams: deliver training-ready labels, feed model failures and long-tail scenarios back into what gets curated and labeled next; at the senior level, lead designs and mentor
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Ensure that your work is performed in accordance with the company's Quality Management System (QMS) requirements and contribute to continuous improvement efforts
Required Skills -
BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field, or equivalent practical experience
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Hands-on experience developing and evaluating 3D perception methods (detection, tracking, sensor fusion or related) with a framework such as PyTorch, including experimentation, error analysis, principled metrics, built on solid fundamentals in 3D geometry, coordinate frames, and camera and lidar calibration
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Strong Python (NumPy, Pandas, SQL) and working C++ where performance matters; experience running large offline workloads with GPU batch inference
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Foundations for data-centric machine learning: how training-data composition shapes model behavior, how to evaluate against ground truth, and how to measure and reduce annotation cost
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Self-driven with a strong sense of responsibility: a quick learner who takes problems from ambiguous to shipped; for the senior level, a system you were responsible for end to end, design documents others built on, and mentoring
Preferred Skills -
Auto-labeling, offline perception, or ground-truth generation for autonomous driving or robotics, including integrations with annotation tools and vendors
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Experience improving models through data: active learning and data flywheels, hard-example mining, uncertainty or disagreement signals, dataset curation
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Lidar SLAM, structure-from-motion, factor-graph optimization (GTSAM, Ceres), or stereo and monocular depth estimation
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Foundation and multimodal models: open-vocabulary detectors, promptable segmentation, vision-language models, embeddings and vector search
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Heavy-truck specifics (articulated vehicles, trailer state estimation, multi-platform calibration) or lane and road-geometry labeling pipelines
Your opportunities joining PlusAI
Work, learn and grow in a highly future-oriented, innovative and dynamic field.
Wide range of opportunities for personal and professional development.
Catered free lunch, unlimited snacks and beverages.
Highly competitive salary and benefits package, including 401(k) plan.