Senior AI Data Science Engineer

Intuitive•Sunnyvale, CA

2h•$152,000 - $257,200•Onsite

About The Position

Advancing embodied AI in robotic surgery requires high-quality data across the robotic platform, the surgical field, and the operating room environment. The Senior AI/Data Science Engineer will advance the data strategy and engineering that turns surgical data into curated, research-ready datasets for AI/ML teams building intelligent surgical systems. Working within Intuitive's Future Forward research organization, you will make judgment calls about what data matters, drive the quality bar, and build the infrastructure that delivers research-ready datasets. This role calls for someone who is equally comfortable getting hands-on with data and designing systems that scale.

Requirements

Master's degree in Computer Science, Data Science, Electrical Engineering, Robotics, or a related technical field with 6+ years of relevant experience, or PhD with 3+ years of relevant experience.
Strong proficiency in Python and SQL for data processing, transformation, and analysis, with familiarity across both relational and non-relational data stores.
Experience with distributed data processing or workflow orchestration frameworks (e.g., Spark, Airflow, or similar).
Track record of designing and building data pipelines for multi-modal datasets (video, time-series, sensor data, structured and unstructured data).
Demonstrated ability to set data quality standards and implement validation frameworks.
Experience working with multiple concurrent data streams from different sources and modalities.
Comfortable working with data that ranges from raw and unstructured to clean and production-grade.
Strong analytical and problem-solving skills in ambiguous, research-oriented environments where you shape the approach, not just execute it.
Clear communicator who can work across engineering, research, and clinical teams.

Nice To Haves

Background in healthcare, medical devices, surgical robotics, or other regulated technical domains.
Hands-on experience with computer vision data workflows.
Understanding of machine learning frameworks and how data quality and structure impact model performance.
Prior work interfacing with embedded systems teams to ingest on-device data streams into broader data infrastructure.
Practical experience with data annotation tools, workflows, and quality control at scale.
Familiarity with ML experiment tracking or data versioning tools (e.g., MLFlow, DVC, Weights & Biases).
Working knowledge of cloud data platforms, containerization (Docker, Kubernetes), and storage solutions.
Experience with robotics middleware, simulation environments, synthetic data generation, or 3D spatial data tooling.
Awareness of data governance in regulated environments (HIPAA, FDA).

Responsibilities

Architect data models and build pipelines that process and integrate surgical data into well-structured datasets across the robotic platform, surgical field, and operating room environment.
Drive the data collection strategy: assess data coverage and plan collection approaches to address evolving research needs.
Own data quality frameworks including validation, anomaly detection, completeness metrics, and lineage tracking so datasets are reliable enough to support training of intelligent surgical systems.
Drive annotation workflows, including label taxonomy, tooling selection, and quality control, in collaboration with surgeons, clinical researchers, and domain experts.
Partner with AI/ML researchers to understand downstream requirements and deliver well-documented datasets ready for model development.
Build tooling that makes it easy for researchers to find, access, and work with surgical datasets.
Collaborate with surgeons, clinical research teams, robotics engineers, and systems engineers to understand how physical actions and perception translate into data, and ensure relevant signals are captured.
Contribute to the broader data strategy for embodied AI, including infrastructure and tooling recommendations aligned with research needs.