PlusAI

Builds AI driving systems for commercial trucks

Research Engineer/Senior Research Engineer - 3D Auto-Labeling and Data Flywheel

Computer VisionConfirmed live in the last 24 hours

Full-timeSanta Clara, CA, USA$130k - $220k /yr

About the Job

Requirements

A Bachelor of Science, Master of Science, or PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field, or equivalent practical experience.
Hands-on experience developing and evaluating 3D perception methods, such as detection, tracking, or sensor fusion, with a framework such as PyTorch, including experimentation, error analysis, and principled metrics, grounded in 3D geometry, coordinate frames, and camera and lidar calibration.
Strong Python skills, including NumPy, Pandas, and SQL, and working knowledge of C++ where performance matters; experience running large offline workloads with GPU batch inference.
Foundations in data-centric machine learning, including understanding how training-data composition shapes model behavior, how to evaluate against ground truth, and how to measure and reduce annotation cost.
A self-driven approach and strong sense of responsibility, with the ability to learn quickly and take problems from ambiguity to delivery.
For the senior level, experience owning a system end to end, producing design documents others build on, and mentoring.

Responsibilities

Develop and evaluate methods that turn multi-sensor recordings into accurate 3D object labels offline, using models and compute unavailable to the vehicle in real time, and measure performance against reviewed ground truth.
Improve label quality and reduce human review by identifying where machine labels fail and addressing the causes in models, sensor fusion, or data; bring state-of-the-art perception and foundation models into offline use and adapt them to the company's vehicles and sensors.
Build and operate fleet-scale labeling pipelines, including batch GPU inference, data extraction and alignment, selecting data for human review, annotation-tool integration, monitoring, throughput and cost management, and regression tests that catch quality drops before data reaches training.
Deliver training-ready labels to modeling teams and feed model failures and long-tail scenarios back into future curation and labeling.
At the senior level, lead designs and mentor.
Perform work in accordance with the company's Quality Management System requirements and contribute to continuous improvement efforts.

Desired Qualifications

Experience with auto-labeling, offline perception, or ground-truth generation for autonomous driving or robotics, including integrations with annotation tools and vendors.
Experience improving models through data, including active learning and data flywheels, hard-example mining, uncertainty or disagreement signals, and dataset curation.
Experience with lidar SLAM, structure-from-motion, factor-graph optimization such as GTSAM or Ceres, or stereo and monocular depth estimation.
Experience with foundation and multimodal models, including open-vocabulary detectors, promptable segmentation, vision-language models, embeddings, and vector search.
Experience with heavy-truck specifics, including articulated vehicles, trailer state estimation, or multi-platform calibration, or lane and road-geometry labeling pipelines.

Salary Range: 130000 to 220000 (Currency: USD) (Pay period: per-year-salary)

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

Improving the models that drive our trucks depends on a data flywheel: among petabytes of fleet recordings, we curate the moments that matter, label them, train and evaluate onboard models, deploy, and collect again. Labeling is where this loop usually stalls, because human labeling does not scale with the fleet. Auto-labeling closes that gap: offline models and compute, free of the vehicle's real-time limits, draft the labels, and people verify and correct only where it matters. This role focuses on 3D obstacle auto-labeling: producing accurate 3D object labels from curated multi-sensor recordings, at a quality that lets reviewers verify rather than draw and at a cost that scales. The underlying problems are general: fusing cameras and lidar into consistent 3D estimates, keeping objects consistent through time and occlusion, and knowing when a machine label is good enough. The role starts with 3D obstacles and can expand into other parts of the data flywheel over time.

We are open to candidates at either the Research Engineer or Senior Research Engineer level. Level will be determined by experience, technical depth, scope of responsibility and demonstrated impact. You do not need experience with every technology in our stack; we value strong fundamentals, a sense of responsibility and the ability to learn.

Responsibilities

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

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

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

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

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

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

Strong Python (NumPy, Pandas, SQL) and working C++ where performance matters; experience running large offline workloads with GPU batch inference

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

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

Experience improving models through data: active learning and data flywheels, hard-example mining, uncertainty or disagreement signals, dataset curation

Lidar SLAM, structure-from-motion, factor-graph optimization (GTSAM, Ceres), or stereo and monocular depth estimation

Foundation and multimodal models: open-vocabulary detectors, promptable segmentation, vision-language models, embeddings and vector search

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.

About the Company

PlusAI

Builds AI driving systems for commercial trucks

PlusAI develops AI driving software for commercial trucks, working with vehicle manufacturers and logistics partners to automate freight transport. Its SuperDrive system combines sensors, onboard computing, and AI models to understand roads, plan maneuvers, and control a truck within defined operating conditions. Truck manufacturers integrate the software into their vehicles, and PlusAI engineers develop and validate the virtual driver for use on freight routes. It also sells autonomous-driving data and development tools through HyperFoundry, and plans to charge for SuperDrive according to truck usage. PlusAI was founded in 2016 and is headquartered in Santa Clara, California.

Company size: 201-500
Company stage: IPO
Headquarters: Santa Clara, CA
Founded: 2016

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