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
Skills Required
- 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 such as detection, tracking, or sensor fusion
- Experience with a framework such as PyTorch
- Strong fundamentals in 3D geometry, coordinate frames, and camera and lidar calibration
- Strong Python experience, including NumPy, Pandas, and SQL
- Working C++ experience where performance matters
- Experience running large offline workloads with GPU batch inference
- Understanding of data-centric machine learning, ground-truth evaluation, training-data composition, and annotation-cost reduction
- Self-driven problem solving and ability to take ambiguous problems through shipment
- For senior-level candidates: end-to-end system ownership, design documentation, and mentoring experience
- Auto-labeling, offline perception, or ground-truth generation experience for autonomous driving or robotics
- Experience integrating annotation tools and vendors
- Experience with active learning, data flywheels, hard-example mining, uncertainty or disagreement signals, or dataset curation
- Experience with Lidar SLAM, structure-from-motion, factor-graph optimization, GTSAM, Ceres, or depth estimation
- Experience with foundation models, open-vocabulary detectors, promptable segmentation, vision-language models, embeddings, or vector search
- Experience with heavy-truck specifics or lane and road-geometry labeling pipelines
Plus Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Plus and has not been reviewed or approved by Plus.
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Leave & Time Off Breadth — Unlimited PTO in addition to company holidays and flexible work arrangements are offered, indicating broad time-off flexibility. This setup signals strong support for taking time away from work.
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Healthcare Strength — Tiered medical, dental, and vision options allow employees to select coverage that fits their needs. This breadth of core health coverage aligns with a comprehensive benefits approach.
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Wellbeing & Lifestyle Benefits — Daily catered lunches at key offices and company-sponsored professional development add meaningful day-to-day and growth-oriented perks. These offerings enhance overall wellbeing and workplace experience.
Plus Insights
What We Do
Plus is a global provider of highly automated driving and fully autonomous driving solutions. Named by Forbes as one of America's Best Startup Employers and Fast Company as one of the World’s Most Innovative Companies, Plus's customers are already operating its product on the road today. Working with one of the largest companies in the U.S., vehicle manufacturers and others, Plus is making transportation safer and greener. Plus has received a number of industry awards and distinctions for its transformative technology and business momentum from Fast Company, Insider, Consumer Electronics Show, AUVSI, and others. For more information, visit www.plus.ai
