Senior/Staff Software Engineer, ML Data

About the Work

• Data Pipeline Architecture: Design and build scalable data ingestion and processing pipelines that turn data streams into targeted training datasets. Lead initiatives to improve data quality, detect anomalies, and manage out-of-distribution examples to ensure robust model training and deployment.
• Cross Functional Leadership: Work across autonomy teams and data infra teams to build effective ML data pipelines and products for ML engineers.
• ML Tooling & Introspection: Develop infrastructure and visualization tools that allow ML researchers to easily introspect data, identify model failure modes, query for new data samples, and understand data distribution shifts.
• Labeling Operations Integration: Collaborate closely with the data operations team to define quality standards, automate quality control (QC), and streamline the feedback loop between model performance and annotation guidelines.
• Active Learning & Data Mining Engines: Lead the engineering effort to operationalize research-grade active learning methods. E.g. build systems that compute embeddings or run inference at scale, manage vector databases, and automatically sample the most informative data points for labeling.

About You

Required Qualifications

• 7+ years of experience with a proven track record of technical leadership architecting and delivering complex, multi-system ML data engineering data systems.
• Education: B.S./M.S. in Computer Science, Artificial Intelligence, Electrical Engineering, Robotics, or equivalent practical experience.
• Understanding of end-to-end ML data pipelines and their interaction with model training and evaluation.
• Strong proficiency in C++ and Python, with petabyte-level data management experience.
• Experience taking data concepts (e.g., "uncertainty sampling") and turning them into stable, 24/7 production services.

Preferred Qualifications

• Prior experience working in large companies with productionized AI systems working on data engines for large scale machine learning.
• Experience in workflow orchestration, introspection UI/UX for data understanding, and ML frameworks for foundation model training.
• Expertise in data-centric AI topics (active learning, pre-training) and their application in autonomous systems.
• You have subject matter expertise and research in one or more of the following areas: Machine Learning, Deep Learning, Robotics , and have some familiarity with the state of the art in ML for autonomous driving and data utilization.

Compensation

Base pay range: $193,930 - $352,290. Eligible for annual performance bonus, equity, and competitive benefits package.