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About This Role
Sunnyvale, CA, United States
Not Remote
Engineering
JOB216053
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
We are building advanced augmented dexterity for next\-generation robotic platforms. As a Senior AI/ML Research Engineer, you will develop and fine\-tune the foundation models—VFMs, VLMs, and VLA models—that let our Embodied\-AI system understand the surgical scene and act within it. Within a hierarchical, multimodal stack, you will own the model layer: adapting large pretrained vision and multimodal models on surgical data to extract anatomy, instruments, actions, and context from intraoperative video, and connecting perception to reasoning and action. Partnering with the broader AI/ML team, you will drive the path from offline research to robust, real\-time performance in the OR.
Working within Intuitive's Future Forward research organization, you will identify, build, and fine\-tune the AI/ML models and algorithms that let us deliver safe and performant embodied\-AI systems. This role calls for someone equally comfortable getting hands\-on with models and data and designing systems that scale.
Roles and Responsibilities
- Develop, fine\-tune, and evaluate the AI/ML models—including foundation and multimodal models—that enable the system to perceive the surgical scene and translate intent and observations into safe, performant behavior.
- Establish strong baselines by reproducing relevant state\-of\-the\-art approaches, then iteratively advance them with in\-house models and components while keeping interfaces stable.
- Build and maintain training and data pipelines that combine real demonstration data with simulation, and establish human\-in\-the\-loop pipelines for continuous model improvement.
- Define and run evaluation for model performance, repeatability, and safe failure/abort behavior, and establish the path from offline evaluation on recorded data to robust, real\-time integration.
- Partner with data and annotation teams to shape label taxonomies, quality control, and the data pipeline that feeds the models.
- Collaborate across AI/ML research, robotics, software, and data engineering to align on interfaces and deliver models that enable rapid prototyping and learning while building toward a product solution.
Qualifications
Minimum Qualifications
- MS or PhD in CS, EE, Robotics, or a related field, with 5\+ years of applied AI/ML research experience in areas such as robot learning, embodied AI, control, or sequential decision\-making.
- Hands\-on experience training policies or models from data, including imitation/behavior cloning and reinforcement learning, and fine\-tuning pretrained models.
- Experience with vision\-action (VA), vision\-language\-action (VLA), or goal/intent\-conditioned models, including models that produce action or control outputs.
- Familiarity with world models and self\-supervised predictive architectures (e.g., JEPA\-style models, MAE, DINO) for learning dynamics and latent representations to support planning and control.
- Comfort building training and evaluation loops over both simulated and real\-world data.
- Strong software and ML\-engineering skills in Python and C\+\+, with proficiency in one or more of PyTorch/TensorFlow/JAX.
- A research\-and\-prototyping mindset: comfortable working in ambiguity, framing open\-ended problems, running rapid experiments, and reading and reproducing recent papers to pull promising techniques into practice.
- Sound judgment about the path from prototype to product: writing code others can build on, knowing when to optimize versus when to move fast, and thinking ahead about data quality, evaluation, and robustness even at the research stage.
- Solid foundations in linear algebra, probability, and optimization, enough to reason about and debug model behavior from first principles.
- Comfort collaborating across a multidisciplinary team (ML, robotics, software, and clinical/domain experts) and communicating tradeoffs and findings clearly.
Preferred Qualifications
- Modern policy architectures: diffusion policies, transformer policies, action chunking (e.g., ACT), and generalist robot policies (RT\-X / OpenVLA\-style).
- DAgger / human\-in\-the\-loop and data\-flywheel pipeline experience.
- Sim\-to\-real transfer and domain randomization (e.g., NVIDIA Isaac Sim).
- Teleoperation, kinematics, and real\-time on\-robot deployment.
- Publications at CoRL, RSS, ICRA, IROS, or NeurIPS.
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: $196,800 USD \- $283,200 USD
Base Compensation Range Region 2: $167,300 USD \- $240,700 USD
Shift: Day
Workplace Type: Onsite \- This job is fully onsite.
Salary Context
This $167K-$240K range is above the median for Research Engineer roles in our dataset (median: $202K across 52 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,823 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Intuitive (Intuitive Surgical), this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $260,000 based on 434 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($204K) sits 22% below the category median. Disclosed range: $167K to $240K.
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 Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
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).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
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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