Sr. AI & Automation Engineer, Robotics

Intuitive•Sunnyvale, CA

23h

About The Position

The Future Forward organization is Intuitive's advanced concepts group. We explore emerging technologies, prototype next-generation solutions, and build software experiences that shape the future of robotic-assisted surgery. Our research team is building advanced augmented dexterity capabilities for next-generation robotic platforms. The Role Our researchers should spend their time on ideas, not on the manual work required to turn those ideas into results on real robots. As a Senior AI & Automation Engineer, you will build the AI tools, automation systems, integrations, and evaluation capabilities that help researchers and engineers train, test, and evaluate models faster and make better decisions. You'll leverage existing AI technologies while developing new solutions, from agent-driven workflows and AI tooling to the APIs, services, and automation frameworks that support engineering and research activities. That could mean agents that set up and run experiments, automation workflows that accelerate testing and evaluation, or tools that sort through logs and video to help teams understand failures and improve outcomes. It's a hands-on engineering role that uses AI to improve how physical AI systems are trained and tested. You'll automate that work; you won't design the models themselves. You'll work closely with ML researchers, simulation engineers, and robotics engineers.

Requirements

Hands-on work with real robots. You've built testing, automation, or evaluation systems that ran on real robot hardware or other physical systems via software interfaces, not only in the cloud or in simulation.
AI and automation you've built and put into daily use. You've built LLM-agent workflows, AI tool integrations, copilots, or automation pipelines for real engineering work and measured whether they helped. Be ready to walk us through one: what it did, where it failed, and what you changed.
Evaluation and experimental rigor. You know how to design experiments and understand the statistics needed to determine whether an improvement is real.
Strong software engineering. You have 5+ years of software engineering experience.
You write production-quality Python, can read C++, and are comfortable with Linux, CI/CD, Docker, containerized services, APIs, and distributed systems.
Strong candidates often come from robotics, autonomous systems, physical AI, or robot- learning research, and have applied AI tooling to real hardware.
You're comfortable working across disciplines in a fast-moving research environment and can explain trade-offs clearly to researchers and engineers alike.
BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field.

Nice To Haves

Robotics simulation, such as NVIDIA Isaac Sim/Lab or MuJoCo
Evaluating AI/ML, robotics, or robot-learning systems
Surgical or medical robotics, including da Vinci Research Kit (dVRK) systems
ROS 2 or other robot middleware
Agent frameworks, tool integrations, or MCP servers adopted by engineering teams
AI assistants, copilots, or productivity tools built for engineers or researchers
AI applied to scientific, engineering, or research workflows
Advanced degree (MS or PhD) preferred.

Responsibilities

Build and integrate AI tools, APIs, and agent workflows that automate engineering and research work across the team.
Automate the training and fine-tuning workflow, from setting up experiments to launching, tracking, and comparing runs, so researchers can reproduce results without hand-built scripts.
Build automation and integration workflows that help researchers and engineers evaluate new ideas in simulation and on real robots, quickly, reliably, and at scale.
Design evaluations and experiments that show, with statistical confidence, whether a change is a real improvement.
Build logging, analysis, and debugging tools that help the team understand results and failures, and that turn robot sessions into data the team can train on.
Make what you build reliable, secure, and safe to run, with human approval where it matters and responsible use of AI throughout.
Evaluate emerging AI tools, models, and technologies, and bring in the ones that deliver measurable impact.
Measure the impact of what you build, document it clearly, and make successful tools part of how the team operates.