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

PlusAI•Santa Clara, CA

About The Position

PlusAI is hiring a Research Engineer or Senior Research Engineer to develop 3D obstacle auto-labeling for autonomous-truck data. The role supports the data flywheel by turning curated, multi-sensor fleet recordings into accurate 3D object labels using offline models and compute. The goal is to produce labels that reviewers can verify rather than draw, while keeping quality high and costs scalable. The work centers on fusing camera and lidar data into consistent 3D estimates, maintaining object consistency over time and through occlusions, and determining when machine-generated labels are reliable enough. The role may expand into other parts of the data flywheel. Level is based on experience, technical depth, scope, and demonstrated impact. The engineer will develop and evaluate labeling methods, improve label quality and reduce human review, and build and operate fleet-scale labeling pipelines. They will deliver training-ready labels and work with modeling teams to feed failures and long-tail scenarios back into data curation. Senior-level responsibilities include leading designs and mentoring. Work must follow the company's Quality Management System requirements and contribute to continuous improvement.

Requirements

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, with a framework such as PyTorch.
Experience with experimentation, error analysis, and principled metrics, supported by fundamentals in 3D geometry, coordinate frames, and camera and lidar calibration.
Strong Python skills, including NumPy, Pandas, and SQL, and working C++ skills where performance matters.
Experience running large offline workloads with GPU batch inference.
Foundational understanding of data-centric machine learning, including how training-data composition affects model behavior, how to evaluate against ground truth, and how to measure and reduce annotation cost.
Self-driven, responsible, and able to learn quickly and take ambiguous problems through to delivery.
For the senior level: experience owning a system end to end, producing design documents others built on, and mentoring.

Nice To Haves

Auto-labeling, offline perception, or ground-truth generation experience for autonomous driving or robotics, including annotation-tool and vendor integrations.
Experience improving models through data, including active learning, data flywheels, hard-example mining, uncertainty or disagreement signals, and dataset curation.
Experience with lidar SLAM, structure-from-motion, factor-graph optimization (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, or vector search.
Heavy-truck experience, such as articulated vehicles, trailer state estimation, or multi-platform calibration, or experience with lane and road-geometry labeling pipelines.

Responsibilities

Develop and evaluate offline methods that generate accurate 3D object labels from multi-sensor recordings, and measure performance against reviewed ground truth.
Identify and address causes of machine-label failures in models, sensor fusion, or data to improve label quality and reduce human review.
Bring state-of-the-art perception and foundation models into offline workflows and adapt them to PlusAI vehicles and sensors.
Build and operate fleet-scale labeling pipelines, including batch GPU inference, data extraction and alignment, human-review selection, annotation-tool integration, monitoring, throughput and cost management, and regression tests.
Deliver training-ready labels and collaborate with modeling teams to feed model failures and long-tail scenarios into future data curation and labeling.
At the senior level, lead designs and mentor others.
Perform work in accordance with the company's Quality Management System requirements and contribute to continuous improvement efforts.