Staff Software Engineer - AI

Company Description

At Intuitive, we are united behind our mission: we believe that minimally invasive care is life-enhancing care. Through ingenuity and intelligent technology, we expand the potential of physicians to heal without constraints.
As a pioneer and market leader in robotic-assisted surgery, we strive to foster an inclusive and diverse team, committed to making a difference. For more than 25 years, we have worked with hospitals and care teams around the world to help solve some of healthcare's hardest challenges and advance what is possible.
Intuitive has been built by the efforts of great people from diverse backgrounds. We believe great ideas can come from anywhere. We strive to foster an inclusive culture built around diversity of thought and mutual respect. We lead with inclusion and empower our team members to do their best work as their most authentic selves.
Passionate people who want to make a difference drive our culture. Our team members are grounded in integrity, have a strong capacity to learn, the energy to get things done, and bring diverse, real world experiences to help us think in new ways. We actively invest in our team members to support their long-term growth so they can continue to advance our mission and achieve their highest potential.
Join a team committed to taking big leaps forward for a global community of healthcare professionals and their patients. Together, let's advance the world of minimally invasive care.

Job Description

Primary Function:

We are seeking a highly motivated full-stack software engineer to support our AI Research and Development team throughout the AI product life cycle, from early-stage prototyping to development and testing, ML and Data pipeline maintenance, and deployment on prem or cloud infrastructure. Our team is focused on developing AI agents for a variety of applications leveraging AI foundation models for medical robotics. This role is an exciting opportunity to join a vibrant team, advance the state-of-the-art in AI for surgical robotics, and develop high impact products that enhance the lives of patients throughout the world.

Responsibilities:

• Develop software for prototyping (training, testing, and evaluating) early-stage AI algorithms proposed in the team.
• Develop, maintain, and test software for ML Pipelines throughout the lifecycle (training, fine-tuning, model registry, optimization, deployment, and analytics).
• Develop, maintain, and test software for Data Pipelines throughout the lifecycle (collection, processing, annotation, storage, and mining) and contribute to efforts in creating new datasets.
• Maintain Deep Learning databases and cloud storage solutions.
• Develop, test, and maintain front-end applications to serve AI models to the end-user.
• Provide technical support to internal users and external partners.

Qualifications

Techincal Requirements:

• Minimum Bachelor's or Master’s degree in computer science, electrical and computer engineering, or related fields.
• Minimum (8) years of industry experience in full-stack production software development, with a focus on AI/ML.
• Proficiency in:
  • Back-end: Python, Java, C++, Node.js, Ruby, or equivalent.
  • Databases: relational (e.g., MySQL) and NoSQL (e.g., MongoDB).
  • Front-end: JavaScript (React, Angular, Vue), HTML/CSS, TypeScript.
  • AI/ML tools (e.g., PyTorch, Numpy, SciPy, OpenCV, Pandas, scikit-learn).

• Knowledge of:
  • Cloud platforms (e.g., AWS, GCP, Azure).
  • Containerization (e.g., Docker).
  • API design (RESTful, GraphQL).
  • WebSockets, WebRTC, and real-time communication.

• Experience with Agile development methodologies.

AI/ML Specific Requirements:

• Experience with model training, validation, and deployment.
• Experience with deep learning architectures (e.g., CNNs, RNNs, Transformers).
• Experience with GPU accelerated algorithms and implementations.
• Experience with AI/ML workflow management tools (e.g., Kubeflow, MLflow).
• Knowledge of computer vision and natural language processing concepts and algorithms (supervised, unsupervised, self-supervised, reinforcement learning).
• Knowledge of model explainability and interpretability techniques.
• Knowledge of model optimization methods (e.g., quantization, pruning, knowledge distillation).
• Experience with classical Data Science algorithms (e.g., regression, classical Machine Learning algorithms).

Full-Stack Requirements:

• Knowledge of UI/UX design principles.
• Experience with DevOps practices (e.g., CI/CD, monitoring).
• Experience with back-end frameworks (e.g., Express.js, Django).
• Experienc