Perception Machine Learning Engineer - Continuous Learning

Waymo•San Francisco, CA

2d•Hybrid

About The Position

As a Perception Machine Learning Engineer, you will build the intelligent systems that "see" the world, directly shaping the future of autonomous travel. Within the Perception team, we are tackling some of the most complex, open-ended challenges in autonomous driving. Our models must constantly adapt and improve as our fleet encounters the vast, unpredictable realities of public roads. We are looking for a Machine Learning Engineer to help design and build the automated, closed-loop systems that drive this continuous improvement. In this role, you will be the bridge between model architecture and large-scale data infrastructure. You will leverage active learning and sophisticated data curation strategies to ensure our perception models are always learning from the most informative examples. Crucially, this means managing the entire lifecycle of our data: intelligently selecting novel scenarios from the fleet while continuously pruning our existing corpus to maximize training efficiency.

Requirements

A bachelor’s degree in Machine Learning, Robotics, or Computer Science.
3+ years of professional experience in Machine Learning and/or Computer Vision.
Proven, hands-on experience applying active learning in production environments.
Strong expertise in building large-scale ML data pipelines (mining, extraction, auto-labeling, ingestion).
Deep understanding of data curation—balancing, core set selection, and sampling—to optimize model performance.
Proficiency in Python and deep learning frameworks (PyTorch or JAX).
Strong software engineering skills for writing robust, production-ready code.

Nice To Haves

An advanced degree (MS or PhD) in Machine Learning, Robotics, or Computer Science.
A record of publications at top-tier conferences (e.g., CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, IROS, RSS, AAAI, IJCV, PAMI).
Experience with C++
Experience building data-centric infrastructure from the ground up to accelerate model iteration cycles.

Responsibilities

Architect Infrastructure: Design and scale the data pipelines needed to mine, ingest, and manage massive volumes of sensor data from our fleet.
Drive Model Improvement: Deploy active learning algorithms to continuously identify and select the most impactful data for training, ensuring our large models continuously adapt to new environments with incremental updates.
Ensure Model Quality: Develop methods and recipes for evaluating real-world performance of our models, and detecting regressions in model updates. Develop and maintain ground-truth free performance metrics.
Optimize Data Efficiency: Conduct large-scale experiments focused on data balancing, subset selection, and label quality optimization. Lead automated curation strategies—including smart pruning and downsampling—to minimize dataset bloat and maximize compute efficiency.
Solve Long-Tail Challenges: Develop robust mining, training and evaluation pipelines for rare, safety-critical real-world scenarios.
Innovate with Model Signals: Utilize uncertainty estimation, confidence scores, and embedding space analysis to uncover model blind spots and guide automated data acquisition.
Collaborate Cross-Functionally: Work closely with researchers and operations teams to iterate on the end-to-end model development lifecycle.