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About This Role
Sunnyvale, CA, United States
Not Remote
Engineering
JOB216051
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.
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.
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
We are building advanced augmented dexterity capabilities for next\-generation robotic platforms. As a Staff AI/ML Architect, you will own the end\-to\-end architecture of our applied\-AI system: a hierarchical, multimodal stack in which a high\-level model interprets sensory observations and produces structured intent, and a low\-level policy turns that intent into precise, safe, real\-time control. You will set the technical direction the rest of the AI/ML team builds against, define the interfaces between perception, reasoning, and control, and make the architecture decisions that let us move safely from offline research to real\-time deployment.
Working within Intuitive's Future Forward research organization, you will identify, build and finetune AI/ML models and algorithms and define the architecture that enables us to deliver safe and performant embodied AI systems. This role calls for someone who is equally comfortable getting hands\-on with models and data and designing systems that scale.
Roles and Responsibilities
- Define and own the end\-to\-end architecture for a hierarchical perception, reasoning, and control AI system.
- Specify the contracts between layers: model outputs, policy interfaces, timing budgets, and safety hooks, and keep them stable as components are swapped and upgraded.
- Make the build and modular\-vs\-monolithic calls, evaluate in\-house approaches against the state of the art and set the target architecture.
- Establish the path from offline evaluation on recorded data to real\-time integration, including the continuous\-improvement (human\-in\-the\-loop) data loop.
- Partner across research, engineering, data, and product teams, mentor senior scientists/engineers and raise the engineering bar across the effort.
- Partner with AI/ML researchers, robotics, data engineers, and other stakeholders to deliver a modular architecture that enables rapid prototyping and learning while working toward a product solution.
Qualifications
Minimum Qualifications
- MS or PhD in CS, EE, Robotics, or a related field with 10\+ years building AI/ML models and algorithms for autonomous\-systems software, with demonstrated ownership of an end\-to\-end AI/ML system architecture taken to production or to a rigorous prototype.
- Deep experience with hierarchical, modular AI/ML or robot\-learning stacks, including high\-level reasoning paired with low\-level control policies.
- Strong grasp of real\-time and safety\-critical ML: latency budgets, failure modes, fallback/abort behavior, and interface/contract design across components.
- Hands\-on expertise with modern deep learning and multimodal, vision\-language, LLM\-based architectures, including vision foundation models (VFM), vision\-action (VA), and vision\-language\-action (VLA) models.
- Fluency in one of PyTorch, TensorFlow, JAX, and in Python and C\+\+, is required.
- Working knowledge of world\-model and self\-supervised predictive architectures (e.g., JEPA\-style models, MAE, DINO) and how learned world models inform perception\-and\-control design.
- Solid foundations in linear algebra, probability, and optimization, enough to reason about and debug model behavior from first principles.
- Proven cross\-functional technical leadership (able to align research, engineering, and product stakeholders around an architecture).
Preferred Qualifications
- Background in healthcare, medical devices, surgical robotics, or other regulated technical domains.
- Foundation\-model adaptation and fine\-tuning for embodied robotics tasks.
- Experience delivering AI/ML in a real\-time, safety\-critical domain.
- Imitation learning, DAgger, and/or reinforcement learning at system scale.
- Sim\-to\-real workflows and experience with robotics simulators (e.g., NVIDIA Isaac Sim).
- Regulatory\-aware AI/ML development for regulated, safety\-critical industries.
- Triton kernel and/or CUDA development experiences
- Publications or recognized contributions at venues such as CVPR, NeurIPS, CoRL, RSS, or ICRA.
- Awareness of data governance in regulated environments (HIPAA, FDA).
Additional Information
- Travel: Minimal
- Ways of Working: Onsite, 5 days per week
- Reports to: Govind Payyavula, Senior Managing Principal – Future Forward Research
- Compensation: Competitive salary, annual bonus, equity, and comprehensive benefits
Additional Information
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, protected veteran or disability status, genetic information or any other status protected under federal, state, or local applicable laws.
Mandatory Notices
U.S. Export Controls Disclaimer: In accordance with the U.S. Export Administration Regulations (15 CFR §743\.13(b)), some roles at Intuitive Surgical may be subject to U.S. export controls for prospective employees who are nationals from countries currently on embargo or sanctions status.
Certain information you provide as part of the application will be used for purposes of determining whether Intuitive Surgical will need to (i) obtain an export license from the U.S. Government on your behalf (note: the government’s licensing process can take 3 to 6\+ months) or (ii) implement a Technology Control Plan (“TCP”) (note: typically adds 2 weeks to the hiring process).
For any Intuitive role subject to export controls, final offers are contingent upon obtaining an approved export license and/or an executed TCP prior to the prospective employee’s start date, which may or may not be flexible, and within a timeframe that does not unreasonably impede the hiring need. If applicable, candidates will be notified and instructed on any requirements for these purposes.
We will consider for employment qualified applicants with arrest and conviction records in accordance with fair chance laws.
Preference will be given to qualified candidates who do not reside, or plan to reside, in Alabama, Arkansas, Delaware, Florida, Indiana, Iowa, Louisiana, Maryland, Mississippi, Missouri, Oklahoma, Pennsylvania, South Carolina, or Tennessee.
This position may be filled at a different job level than listed here depending on
business need and/or on the selected candidate’s experience, knowledge and skills.
Compensation will be based primarily on the job level at which the role is filled and the
candidate’s qualifications, consistent with applicable law.
We provide market\-competitive compensation packages, inclusive of base pay, incentives, benefits, and equity. It would not be typical for someone to be hired at the top end of range for the role, as actual pay will be determined based on several factors, including experience, skills, and qualifications. The target compensation ranges are listed.
Base Compensation Range Region 1: $229,600 USD \- $330,400 USD
Base Compensation Range Region 2: $195,200 USD \- $280,800 USD
Shift: Day
Workplace Type: Onsite \- This job is fully onsite.
Salary Context
This $195K-$330K range is above the 75th percentile for AI Architect roles in our dataset (median: $169K across 31 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,823 AI roles we're tracking, AI Architect positions make up 1% of the market. At Intuitive (Intuitive Surgical), this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $212,500 based on 108 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($262K) sits 24% above the category median. Disclosed range: $195K to $330K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Intuitive (Intuitive Surgical) AI Hiring
Intuitive (Intuitive Surgical) has 5 open AI roles right now. They're hiring across Research Engineer, AI Architect, AI/ML Engineer. Based in Sunnyvale, CA, US. Compensation range: $240K - $416K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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