Staff Machine Learning Engineer

Company Description

It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive. As a global leader in robotic-assisted surgery and minimally invasive care, our technologies—like the da Vinci surgical system and Ion—have transformed how care is delivered for millions of patients worldwide.

We’re a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.

The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful—because every improvement we make has the potential to change a life.

If you’re ready to contribute to something bigger than yourself and help transform the future of healthcare, you’ll find your purpose here.

Job Description

Primary Function of Position

As a Staff Machine Learning Engineer, you will be responsible for developing and delivering novel machine learning solutions for multimodal, physical AI, robotic systems. You will work alongside research, engineering, regulatory, product and clinical teams to develop and test algorithms and translate them into robust, validated and scalable medical device software and infrastructures.

Responsibilities

• Independently lead projects to improve key metrics at program-level and product-level of model performance tracked throughout the development life cycle of AI/ ML product solutions

• Design, implement and optimize structured experimentation across model architectures, datasets, and training strategies for vision foundation model-driven solutions

• Update and improve primary machine learning models - inform and follow through required data collections and testing for both internal and external regulatory milestones.

• Optimize and validate models for integration into production systems, ensuring performance in real-world clinical settings. This position will be primarily focused on vision foundation models, but may extend to other (language, action, predictive, physical AI, etc)

• Design and maintain end-to-end ML pipelines spanning data collection, data curation, synthetic data generation, model training, evaluation, validation, and integration into robotic platform workflows. Implement semi-supervised and self-supervised methods and pipelines to reduce image annotation burden. Develop tools, pipeline, framework and applications to be deployed and scaled.

• Collaborate with physicians and product teams to ensure clinical relevance, robustness and usability of models

• Stay up to date with latest CV/ML/VLA/encoding/predictive/generative AI literature, use this to inform research and product direction

• Participate in integration of new ML/CV algorithms into existing and future robotic platforms; delivering high-quality, production-ready code in a dynamic and fast-paced environment.

Qualifications

Required Qualifications

• PhD or Master’s degree in Computer Science, Electrical Engineering, Robotics, Machine Learning, Computer Vision, or a related technical field.

• 7+ years of industry experience developing machine learning, robotics, computer vision, simulation, or autonomous-systems software; or 4+ years of industry experience with a PhD

• Previously deployed CV/ML projects to users/ customers

• Fluent in Python and experience with ML frameworks and models (PyTorch, TensorFlow, JAX), C/C++ (proficiency)

• Strong background with hands-on experience of modern deep learning and multimodal vision-language, transformer-based architectures, including self-supervised and predictive foundation-model adaptation and fine-tuning (e.g. JEPA-style models, MAE, DINO, contrastive learning, etc) for improved perception

• Proven cross-functional technical leadership in clinical translation (ability to align research, engineering and product stakeholders around an architecture)

• Demonstrated ability to lead technically complex ML engineering projects from concept through implementation, validation, and integration with users, engineering platforms, or deployed systems.

Preferred Qualifications

• Familiarity with regulatory-aware AI/ML development, design controls, safety risk analysis, software validation, quality systems, HIPAA, FDA expectations, or data governance in healthcare, medical devices, surgical robotics or other