Lead Data Engineer, Physical Intelligence

About Us:

Automated Tire (ATI) is on a mission to reinvent tire changing and wheel balancing using cutting-edge robotics. We are transforming a process that hasn’t fundamentally changed in decades into an automated, high-performance system built for the future of automotive service.

Founded by experienced entrepreneurs and backed by leading players across the automotive and tire industries, ATI is building technology that will redefine how cars are serviced. Our team combines deep robotics expertise with real world deployment, moving fast from prototype to production and scaling solutions directly in the field.

If you are excited by hands-on robotics, real-world impact, and the challenge of building category-defining technology from the ground up, ATI is the place to do the most meaningful work of your career.

Key Responsibilities:

• Define the architecture for the entire data and AI platform — from ingestion and transformation to training, inference, and continuous feedback loops — powering a new generation of physical AI products.
• Design and build full-stack data and ML pipelines: real-time and batch ingestion, feature engineering, model training and evaluation, inference services, and monitoring systems.
• Lead the development of scalable streaming and batch data systems optimized for robotic and embedded environments, ensuring high throughput, low latency, and fault tolerance.
• Establish strong data foundations — contracts, schema standards, validation, anomaly detection, lineage, and versioning — across all data sources (robotics, sensors, cameras, telemetry).
• Integrate tightly with robotics, perception, and control systems, enabling real-time feedback loops, edge intelligence, and sensor fusion.
• Implement robust observability: build dashboards, alerts, and metrics for pipeline performance, data drift, model health, and system reliability.
• Collaborate cross-functionally with product, hardware, and CV teams to align technical decisions with product goals and user impact.
• Hire, mentor, and grow a world-class team of data and ML engineers, set technical standards, and guide architectural decisions.
• Shape the strategic roadmap for how ATI leverages data and AI to unlock new capabilities in robotics and physical intelligence.

Requirements

• 10+ years of experience across data infrastructure, ML systems, or AI engineering roles — with evidence of driving architecture and leading teams.
• Deep expertise in building end-to-end data & ML systems, from ingestion to production inference, particularly in real-time / streaming environments.
• Strong software engineering background (Python, Scala, Java, or similar) and experience with data processing frameworks (Spark, Flink, Beam, etc.).
• Experience in designing and building feature stores, model registries, or MLOps systems (e.g. MLflow, Feast, Tecton).
• Hands-on with orchestration and pipeline tooling (Airflow, Dagster, Prefect), and experience scaling complex workflows.
• Deep SQL & database skills, data modeling, schema design, partitioning, and query optimization.
  Solid background in deploying and monitoring ML models — inference at scale, model drift, performance optimization.
• Experience interacting with robotics, sensor, or computer vision data, and familiarity with edge / embedded constraints.
• Experience with cloud infrastructure (AWS, GCP, Azure) and modern data ecosystems (data lakes, containers, serverless).
• Excellent communication and collaboration skills; ability to translate technical constraints into product impact.

Preferred Qualifications:

• Background in robotics, autonomous systems, SLAM, 3D perception, or sensor fusion.
• Experience deploying on embedded / constrained hardware (e.g., edge inference, GPU/TPU at the edge).
• Familiarity with data lake frameworks (Delta Lake, Iceberg, Hudi).
• Experience in closed-loop feedback systems, online learning, or real-time adaptation.
• Prior experience in startup environments, building from scratch, and owning cross-functional scope.