ML Research Engineer Jobs
ML Research Engineer jobs are open across technology, healthcare, finance, and defense, from new-grad to staff and principal level, with specializations in foundation models, reinforcement learning, and computer vision. Find a role that fits from the openings below and apply directly.
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INTRODUCTION
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.
ROLE AND RESPONSIBILITIES
Primary Function of Position
We are building advanced augmented dexterity capabilities for next-generation robotic platforms. As a Senior AI/ML Research Engineer (Computer Vision), you will develop the perception models that let our Embodied-AI system understand the surgical scene. Working within a hierarchical, multimodal stack—where a high-level model interprets sensory observations into structured intent and a low-level policy turns that intent into precise, safe, real-time control—you will focus on the vision layer: designing, training, and evaluating models that extract anatomy, instruments, actions, and surgical context from intraoperative video. You will partner with the broader AI/ML team to define how perception feeds reasoning and control, and you will drive the research-to-deployment path for your models, taking them from offline experimentation to robust, real-time performance in the OR.
Working within Intuitive's Future Forward research organization, you will identify, build and finetune the AI/ML models and algorithms 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.
- Develop temporal models for activity and workflow understanding: event/state recognition and fine-grained temporal action segmentation.
- Benchmark in-house models against the state of the art and recommend the target perception architecture.
- Define the perception input/output specification and demonstrate offline feasibility on recorded data.
- Stand up a continuous-improvement loop (discrepancy flagging, active learning, human-in-the-loop relabeling) and the tooling/UI needed for offline evaluation and the path to real-time use.
- Partner with annotation and data teams to shape label taxonomies, QC, and the data pipeline that feeds the AI/ML models.
- Establish the path from offline evaluation on recorded data to real-time integration, including the continuous-improvement (human-in-the-loop) data loop.
- Partner with AI/ML researchers, robotics, data engineers, and other stakeholders to deliver a perception layer that enables rapid prototyping and learning while working toward a product solution.
MINIMUM QUALIFICATIONS
- MS or PhD in CS, EE, Robotics, or a related field, with 5+ years of applied computer-vision research experience.
- Strong grasp of modern CV and deep-learning fundamentals: CNNs and vision transformers, segmentation, detection, tracking, and representation/self-supervised learning.
- Demonstrated work in video understanding, including temporal action segmentation, action/phase recognition, and video segmentation.
- Hands-on experience with modern video architectures, including video transformers and self-supervised video pretraining.
- Exposure to vision-action (VA) / vision-language-action (VLA) models and world-model / self-supervised predictive architectures (e.g., JEPA-style models, MAE, DINO) for learning visual representations and dynamics.
- Experience working with large, messy, real-world video datasets at scale.
- Strong software and experimentation skills in Python and C++, with proficiency in one or more of PyTorch/TensorFlow/JAX, and the ability to stand up clean, reproducible experiments and run the full loop (data curation, augmentation, loss design, metrics, error analysis).
- 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
- Background in healthcare, medical devices, surgical robotics, or other regulated technical domains.
- Sim-to-real workflows and experience with robotics simulators (e.g., NVIDIA Isaac).
- Experience with structured, ontology- or taxonomy-based labeling frameworks for fine-grained activity.
- Multimodal fusion of video with sensor, telemetry, and system-log streams.
- Designing annotation pipelines, QC processes, and active-learning loops.
- Real-time / edge inference optimization (e.g., TensorRT, NVIDIA Jetson).
- Fine-grained interaction and object-relationship modeling.
- Relevant peer-reviewed publications (CVPR, ICCV, ECCV, NeurIPS, etc.).
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.
LOCATION
Sunnyvale, CA, United States
Not Remote
JOB TYPE
Engineering
JOB216052
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Find ML Research Engineer JobsML Research Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Apple49

- Scale AI41

- Meta19

- Microsoft19

- ByteDance15

Top Industries Hiring
- Technology & Software169
- Electronics & Hardware76
- Artificial Intelligence71
- Science & Research34
- Banking & Financial Services26
What Employers Look For
The qualifications that appear most often in ML research engineer jobs.
- Advanced degree in machine learning, computer science, or a closely related field
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX
- Experience designing, training, and evaluating large-scale neural network models
- Publication record or demonstrated original research contributions in ML or AI
- Strong mathematical foundation in statistics, linear algebra, and optimization
- Experience with distributed training, cloud compute platforms, or MLOps tooling
Tips for Your ML Research Engineer Job Search
Tailor your resume to the research stack
List the specific frameworks you've used in production or published research, PyTorch, JAX, or TensorFlow, alongside your model architectures. Hiring managers scan for these before reading your experience bullets, so front-load them in a skills or technical summary section.
Link publications and open-source contributions
Attach a Google Scholar profile or GitHub repository to every application. ML research teams weigh published papers and reproducible code heavily, and a single first-author paper at a top venue can move you past the resume screen faster than years of industry experience alone.
Apply early to roles that fit
Migrate Mate lists ml research engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter by research versus applied focus
Job titles blur the line between pure research and applied engineering. Read each listing for keywords like 'publish,' 'novel methods,' or 'production serving.' Targeting roles that match your actual focus, foundational versus deployment, improves your interview fit and reduces mismatched offers.
Prepare a research talk, not just a portfolio
Most ML research engineer loops include a technical presentation on your own work. Practice explaining your problem setup, baselines, ablations, and results in 20 minutes. Teams evaluate how you reason about failure modes, not just whether your final numbers were strong.
Negotiate compute resources alongside compensation
GPU cluster access, cloud compute budgets, and dataset licensing rights affect your ability to do the job. Raise these in the offer stage alongside salary. Teams that can't answer concretely often signal a mismatch between the research mandate and actual infrastructure investment.
ML Research Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ml research engineers?
The companies hiring the most ml research engineers right now include Apple, Scale AI, and Meta, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of June 2026. Demand is concentrated at large technology companies and well-funded AI labs, though defense contractors and healthcare AI startups have expanded hiring meaningfully in recent cycles.
How many ml research engineer jobs are remote?
About 14% of ml research engineer openings are fully remote or hybrid as of June 2026, reflecting strong demand from teams that operate across distributed research centers. Roles focused on language modeling and data-centric AI tend to offer the most remote flexibility, while positions requiring access to proprietary hardware clusters or on-site collaboration with product teams are more likely to require in-person presence.
How do you become a ml research engineer?
Start by building a strong foundation in mathematics, particularly linear algebra, probability, and calculus, alongside proficiency in Python and a major deep learning framework. Complete graduate-level coursework or a master's or doctoral program in machine learning or computer science. Contribute to open-source projects, replicate published papers, and aim to publish or present original work. Internships at research labs or AI teams convert directly into full-time roles and are often the most direct path in.
Can you get hired as a ml research engineer without much experience?
Yes, though the bar is high without a graduate degree or publications. The most effective entry points are research internships during a master's or doctoral program, open-source contributions to widely used ML libraries, and strong performance in research-adjacent roles such as data scientist or ML engineer. Replicating landmark papers with novel extensions and sharing results publicly demonstrates research capability in the absence of a formal publication record.
What does the ml research engineer interview process look like?
Most loops include a recruiter screen, a coding round focused on data structures and ML fundamentals, a research discussion where you walk through a past project in depth, and a technical presentation on original work. Some teams add a take-home research problem or a whiteboard session on model design and experimental methodology. Final rounds typically involve senior researchers evaluating your ability to scope a research problem and reason clearly about tradeoffs.
Where can I find and apply to ml research engineer jobs?
You can find and apply to ml research engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the listings to find roles that match your focus area and experience level, then apply directly to each listing that fits.
See All 418+ ML Research Engineer Jobs
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