The Staff Data Engineer, AI and Robotics will join the AI Research team within the Autonomous Robotics Center (ARC) . This role sets the technical direction for the robotics data backbone that enables scalable robot learning in manufacturing — from data capture and curation through versioning, serving, and auditing. Your work will make model development reproducible, testable, and production-ready, while establishing the infrastructure standards and operating patterns that accelerate robotics AI across programs.
This is a senior technical leadership role in robotics and machine learning infrastructure , focused on multimodal robotic datasets and continuous model iteration. You will work across AI research, robotics engineering, manufacturing, and validation teams to turn real-world robot behavior and failures into high-quality training data, robust production systems, and durable platform capabilities used broadly across the organization.
What You’ll Do
-
Define and drive the technical vision for multimodal robotics data infrastructure spanning vision, depth, force/torque, joint states, events, and metadata across lab and plant-adjacent environments.
-
Architect and scale reliable data capture, ingestion, and serving pipelines that support robot learning workflows from experimentation through production deployment.
-
Establish reproducible data logging and replay frameworks, including ROS 2 bagging where applicable, to enable debugging, regression testing, root-cause analysis, and dataset creation at scale.
-
Own the strategy for dataset lifecycle management, including versioning, lineage, provenance, governance, retention, and quality gates, to support trustworthy model training and evaluation.
-
Lead the integration of experiment tracking, model/data traceability, and auditability patterns so teams can compare runs, reproduce results, and understand system changes over time.
-
Design and implement MLOps automation patterns, including CI/CD/CT-style pipelines for ML systems, that reduce manual effort and improve deployment confidence for robotics AI updates.
-
Partner with AI/ML, planning, validation, and plant teams to define data contracts such as schemas, labeling standards, and failure taxonomies, and convert field failures into curated training datasets and measurable learning loops.
-
Influence architecture across adjacent systems and mentor engineers on best practices in data engineering, ML infrastructure, observability, and production reliability.
-
Drive cross-functional technical decisions, balancing research velocity with platform robustness, governance, and long-term maintainability.
What You’ll Need (Required Qualifications)
-
B.S. or M.S. in Computer Science, Computer Engineering, Data Engineering , or a related field.
-
8+ years of experience building production data systems and/or ML infrastructure, including practical experience supporting training pipelines end-to-end.
-
Strong proficiency in Python and at least one of: C++ , Scala , or Java .
-
Demonstrated engineering discipline in testing, documentation, system design, and operational reliability.
-
Experience with dataset versioning, lineage, and reproducibility tooling such as DVC or equivalent approaches.
-
Experience with experiment tracking and model registry patterns such as MLflow or equivalent tools.
-
Experience designing technical systems that support multiple stakeholders and use cases, with the ability to influence architecture beyond an individual project.
-
Ability to work onsite with hardware and robotics teams, and to design pipelines that handle real-world robotic logging constraints such as bandwidth limits, dropped frames, and timing drift.
What Will Give You a Competitive Edge (Preferred Qualifications)
-
Hands-on robotics logging and replay experience, including ROS 2 bags and system telemetry pipelines.
-
Experience with simulation-to-real data workflows and dataset synthesis strategies.
-
Familiarity with data governance requirements and auditability in safety-adjacent or safety-critical systems.
-
Experience building tools to support data labeling workflows, quality assurance, and active learning loops.
-
Experience serving as a technical lead, setting engineering standards, and mentoring senior or mid-level engineers across complex initiatives.
