Machine Learning Engineer

• Ways of Working:
  Set Schedule - This job will be onsite weekly, the percentage of onsite work will be defined by the leader.
• Employee Type:
  Employee
• Min. Salary Region 1: 164000 USD
• Global Job Level (HCM):
  Professional 3 (9)
• Min. Salary Region 2: 139400 USD

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.

Role

We are looking for a talented individual to join our growing machine learning and data science team to help provide creative ways to develop new technology focused on surgical workflow and performance for next generation robotic surgery platforms.

As a Machine Learning Engineer, you will work at the intersection of machine learning and engineering (i.e., MLOps) to contribute to innovative digital solutions leveraging Surgical AI/ML technologies. Immediate projects and responsibilities may include:

• Integrating machine learning into digital products and services by working cross-functionally across engineering, data science, and machine learning teams
• Developing automated workflows and tools to curate datasets and facilitate training of deep learning models
• Working closely with Machine Learning and Data/Software Engineering teams to develop efficient processes for model development/deployment for various applications.
• Help support and manage a growing cloud infrastructure for MLOps

What you’ll need to be successful:

• M.S. or Ph.D. in computer science, electrical and computer engineering, or related fields.
• Minimum 3 years of industry experience developing productionized code in machine learning, data engineering, or related field for AI applications
• Excellent communication skills both written and verbal
• A desire to work in a high‑energy, focused, small‑team environment with a sense of shared responsibility and shared reward
• Interest in early research and development through to product roll‑out in the fields of surgical AI and surgical robotics
• Hands‑on experience with ML frameworks, such as PyTorch, Tensorflow, or similar
• Knowledgeable about MLOps platforms (Domino Data Labs) and/or ML CI/CD workflows to manage datasets and model training, deployment, and monitoring
• Experience with MLOps tools like MLFlow, Kube Flow, W&B, etc
• Experience with cloud compute environments such as AWS, GCP, etc
• Experience with both edge and cloud deployments, focused on automation, scalability, and robustness
• Experience with Python and SQL
• Experience with Git e.g github, gitlab, bitbucket, etc
• Ability to travel domestically and internationally (5-10%)
• Experience with successfully launching ML models into production
• Experience supporting large multi‑modality dataset including image/video
• Experience with in healthcare
• Experience with federated learning

Due to the nature of our business and the role, please note that Intuitive and/or your customer(s) may require that you show current proof of vaccination against certain diseases including COVID‑19. Details can vary by role.

Intuitive is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified applicants and employees, and prohibit discrimination and harassment of any type, without regard to race, sex, pregnancy, sexual orientation, gender identity, national origin, color, age, religion,…