Software Engineer (SE / Sr SE), Applied ML & Data Mining

Finding the right data is central to improving autonomous-driving models. Among petabytes of fleet data, you will develop methods that identify and rank the most valuable moments for training and evaluation, then turn those methods into reliable tools that autonomy and ML engineers use to search, review, and curate datasets. You will work at the intersection of applied machine learning, information retrieval, large-scale data processing, and product engineering. We welcome candidates with ML or data-mining foundations who are excited to grow across scalable systems and the product stack. We are open to candidates at either the Software Engineer or Senior Software Engineer level. Level will be determined by experience, technical depth, scope of ownership, and demonstrated impact. You do not need experience with every technology in our stack; we value strong fundamentals, ownership, and the ability to learn. Responsibilities: Develop and evaluate mining, retrieval, and ranking methods using signals such as model confidence, disagreement, embeddings, anomalies, temporal behavior, and learned representations Build and evolve semantic image/video/scenario search, including text-to-image/video and image-to-image or video-to-video retrieval, vector search, metadata and temporal or spatial filters, task-specific ranking, and search quality, freshness, latency, and reliability Build and operate distributed mining, inference, and indexing pipelines over fleet-scale imagery, video, time-series, and autonomy-system data, including GPU batch inference, embedding generation, reproducible candidate datasets, and reliable index refreshes Design and ship mining products end to end: Python APIs and services, relational data models, asynchronous jobs, modern TypeScript/React search and review experiences, deployment, access control, testing, observability, and production reliability Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts

BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience Hands-on experience developing and evaluating machine-learning, data-mining, computer-vision, or information-retrieval methods with a framework such as PyTorch or TensorFlow, including experimentation, error analysis, and principled metrics Foundations for data-centric machine learning: an understanding of model uncertainty and evaluation, embeddings and similarity search, and how training-data composition shapes model behavior Self-driven with a strong sense of ownership: a quick learner who is eager to take responsibility and drive projects forward end to end

Production full-stack experience spanning backend services and REST APIs, relational data modeling and SQL, and modern JavaScript or TypeScript frontend development using React or a comparable framework Experience improving models through data — training or fine-tuning, active learning and data flywheels, hard-example mining, uncertainty or disagreement signals, dataset curation — ideally in autonomous driving, robotics, or perception Experience with embedding and multimodal models (CLIP-style models, VLMs), vector databases (Milvus, FAISS, pgvector), or GPU batch inference at scale

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