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About This Role
Senior or Staff Machine Learning Scientist
Cleveland Clinic is seeking a Senior or Staff Machine Learning Scientist to develop and translate advanced AI solutions across surgery, perioperative care, clinical research, and healthcare operations.
Embedded within the Department of Plastic Surgery and InSITE Research Group, this individual will work directly with surgeons, clinicians, and investigators while collaborating with enterprise AI, engineering, analytics, cloud, and MLOps teams. The role offers a distinctive opportunity to develop AI for the operating room and perioperative environment using surgical video, medical imaging, physiologic signals, sensor data, robotics, electronic health record data, and patient-reported outcomes.
We are seeking a scientist with deep expertise in computer vision, video understanding, or multimodal machine learning, complemented by experience in at least one adjacent area such as generative AI, robotics, predictive modeling, or clinical natural language processing. The successful candidate will contribute across data engineering, model development, rigorous evaluation, clinical validation, and production deployment, helping move high-value projects from clinical need to scalable implementation.
Key Responsibilities
- Design, develop, and validate machine learning models for surgical, perioperative, clinical, and operational applications.
- Advance computer vision, multimodal learning, generative AI, predictive modeling, and real-time clinical decision support.
- Develop machine learning systems using time-synchronized surgical video, medical imaging, physiologic waveforms, device and sensor streams, robotic telemetry, longitudinal electronic health record data, and patient-reported outcomes.
- Create methods to recognize surgical phases, anatomy, instruments, events, workflow, and the evolving state of the operating room.
- Build and extend reusable technical infrastructure for surgical intelligence, including multimodal data ingestion and synchronization, data quality controls, annotation and labeling workflows, dataset versioning, reproducible training pipelines, model evaluation, prospective validation, deployment, and post-deployment monitoring.
- Explore physical AI applications that connect perception, prediction, and action through intelligent surgical systems, robotics, sensor-enabled environments, and human-machine collaboration.
- Collaborate on emerging approaches such as world models, vision-language-action models, and reinforcement learning for surgical and perioperative environments.
- Develop AI solutions addressing surgical safety, quality, patient-reported outcomes, operations, documentation, and perioperative efficiency.
- Translate clinical and operational needs into technically rigorous projects with clear data requirements, validation strategies, and measures of success.
- Provide technical leadership across multidisciplinary projects by shaping system architecture, establishing evaluation standards, reviewing technical work, and coordinating clinicians, researchers, data engineers, ML engineers, enterprise platform teams, and external collaborators from early development through implementation.
- Contribute to peer-reviewed publications, presentations, grants, and collaborative research.
- Apply responsible and human-centered AI practices throughout development and clinical translation, including privacy-preserving data use, bias and subgroup evaluation, interpretability appropriate to the use case, uncertainty communication, human-factors assessment, documentation, and clearly defined human oversight.
Required Qualifications
- PhD in computer science, engineering, data science, biomedical informatics, robotics, applied mathematics, statistics, or a related quantitative field.
- Advanced knowledge of machine learning and deep learning, with demonstrated depth in at least one relevant technical area and the ability to select appropriate methods, design rigorous experiments, diagnose failure modes, and evaluate whether model performance is sufficient for the intended clinical or operational use.
- Strong proficiency in Python and demonstrated ability to develop maintainable, tested machine learning software using modern deep learning frameworks such as PyTorch, TensorFlow, or JAX; experience with relevant model and data ecosystems, including Hugging Face where appropriate.
- Demonstrated experience developing and validating machine learning models using complex real-world data.
- Ability to communicate effectively and collaborate with clinicians, researchers, engineers, and operational leaders.
- Strong record of independent technical problem-solving and multidisciplinary teamwork.
Preferred Qualifications
- Experience with surgical video, medical imaging, physiologic signals, sensor fusion, robotic systems, electronic health record data, or patient-reported outcomes.
- Experience developing models for real-time or safety-sensitive environments.
- Experience with temporal modeling, surgical phase recognition, or human-machine interaction.
- Experience with cloud AI platforms, Databricks, scalable data pipelines, or distributed computing.
- Experience with MLOps, including model deployment, monitoring, reproducibility, and lifecycle management.
- Experience translating AI research into clinical, operational, or other real-world environments.
- Familiarity with responsible AI, privacy, security, data governance, and validation in regulated settings.
- Strong publication, applied research, or AI product development record.
- Experience coordinating engineers, data scientists, or external technical collaborators.
The Ideal Candidate
The ideal candidate is an accomplished AI scientist who wants to work closely with surgeons and clinical teams on consequential healthcare problems. This individual will bring recognized expertise in a core technical discipline while remaining versatile enough to contribute across modeling, multimodal data infrastructure, validation, deployment, and research.
The successful candidate will be intellectually curious, collaborative, and comfortable working in the operating room and perioperative environment. They will understand that meaningful surgical AI requires more than model performance. It requires clinical immersion, thoughtful integration of multiple data streams, prospective validation, responsible implementation, and effective human-machine interaction.
Why Join Us
- Develop AI directly within the operating room and perioperative environment.
- Advance computer vision, multimodal AI, physical AI, surgical intelligence, and personalized decision support
- Build intelligent systems integrating surgical video, imaging, physiologic, sensor, robotic, and clinical data.
- Shape emerging applications of robotics, embodied intelligence, and human-machine collaboration in surgery.
- Develop AI across the continuum from research concept to clinical or operational implementation.
- Build platforms that can extend across projects, procedures, and specialties.
- Collaborate with leading clinical, research, enterprise AI, engineering, and external technology teams.
- Combine publication-quality research with meaningful clinical impact.
Cleveland Clinic is committed to using AI to improve patient outcomes, personalize care, reduce administrative burden, and increase healthcare efficiency. Our AI culture emphasizes technical excellence, curiosity, collaboration, responsible innovation, and measurable clinical impact.
Learn more about Cleveland Clinic
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About Us
Cleveland Clinic's vision is to become the best place for care and the best place to work in healthcare. We are committed to providing a safe, stable, and financially fulfilling work environment. Cleveland Clinic is ambitiously investing in growth. Being a physician-led organization means doing what is best for the patients, every day.
Cleveland Clinic is one of the world's largest and busiest health centers. Patients come to Cleveland Clinic from all over the world. We offer advanced treatment for all illnesses and disorders of the body.
Cleveland Clinic is a nonprofit, multispecialty academic medical center that's recognized in the U.S. and throughout the world for its expertise and care. Cleveland Clinic integrates clinical and hospital care with research and education. Located in Cleveland, Ohio, it was founded in 1921 by four renowned physicians with a vision of providing outstanding patient care based upon the principles of cooperation, compassion and innovation. Cleveland Clinic has pioneered many medical breakthroughs, including coronary artery bypass surgery and the first face transplant in the United States. Among Cleveland Clinic's 80,642 employees worldwide are more than 5,743 salaried physicians and researchers, and 20,166 registered nurses and advanced practice providers, representing 140 medical specialties and subspecialties. Cleveland Clinic is a 6,690-bed health system that includes a 173-acre main campus near downtown Cleveland, 23 hospitals, more than 270 outpatient facilities, including locations in northeast Ohio; southeast Florida; Las Vegas, Nevada; Toronto, Canada; Abu Dhabi, UAE; and London, England. In 2025, there were 15.9 million total outpatient visits, 343,000 hospital admissions and observations, and 336,000 surgical cases throughout Cleveland Clinic's health system. Patients came for treatment from every state and 185 countries.
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Information for Candidates
Candidates will only be asked to provide personal documents once an offer of employment has been made and accepted. Recruitment scams are becoming increasingly common online, with false advertisements, impersonation, and requests for payment or personal details claiming to come from reputable organizations. Please be assured that our physician recruiters will never ask for payment from candidates at any stage of the recruitment or offer process. Additionally, all email communication will come from a Cleveland Clinic email address.
Disclaimer
The policy of Cleveland Clinic Health System and its system hospitals (Cleveland Clinic Health System) is to provide equal opportunity to all of our caregivers and applicants for employment in our drug free environment. All offers of employment are followed by testing for controlled substances.
Cleveland Clinic Health System administers an influenza prevention program. You will be required to comply with this program, which will include obtaining an influenza vaccination on an annual basis or obtaining an approved exemption.
Decisions concerning employment, transfers and promotions are made upon the basis of the best qualified candidate without regard to color, race, religion, national origin, age, sex, sexual orientation, marital status, ancestry, status as a disabled or Vietnam era veteran or any other characteristic protected by law. Information provided on this application may be shared with any Cleveland Clinic Health System facility.
Cleveland Clinic is pleased to be an equal employment opportunity employer .
The salary range displayed in this job posting reflects the anticipated salary range for new physicians hired into full-time (100% FTE) positions. This range is generally aligned with or below the 50th percentile of nationally recognized compensation benchmarks by specialty. A successful candidate's actual compensation will be determined in accordance with fair market value, considering factors such as professional experience, clinical expertise, board certification, work history, and FTE. This stated range excludes the value of Cleveland Clinic's comprehensive benefits package, which includes healthcare, dental, vision, retirement, and other offerings.
Pay Range
Minimum salary: $100
Maximum salary: $200
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cleveland Clinic, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Cleveland Clinic AI Hiring
Cleveland Clinic has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Cleveland, OH, US. Compensation range: $230K - $230K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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