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About This Role
Site: The Brigham and Women's Hospital, Inc.
Mass General Brigham relies on a wide range of professionals, including doctors, nurses, business people, tech experts, researchers, and systems analysts to advance our mission. As a not-for-profit, we support patient care, research, teaching, and community service, striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
APPLY BY EMAIL ONLY - APPLICATION INSTRUCTIONS BELOW
One full-time (40 hours/week) hybrid position is available. When applying, please indicate the track to which you are applying:
- Computational Track (Ph.D. in a STEM discipline)
- Clinician-Scientist Track (M.D., Ph.D, or equivalent clinical degree)
Application Deadline: Applications will be reviewed on a rolling basis beginning October, 2026 and accepted until the position is filled.
Anticipated Start Date: February, 2027 (flexible)
Description
The NEIL AND ELISE WALLACE STRATUS Center for Medical Simulation at BWH is committed to advancing medical education, patient safety, and healthcare outcomes through cutting-edge applications of medical simulation technologies supported by innovative curricula, contemporary educational methodologies, continuous improvement, and rigorous, multidisciplinary research.
At STRATUS, the Medical AI & Cognitive (MAICE) Lab, led by Dr. Roger Dias, MD, PhD, MBA, brings together a multidisciplinary team of clinicians, engineers, psychologists, computer scientists, and educators, including collaborations with several scientists and institutions, nationally and internationally. The core mission of our research lab involves the use of cutting-edge technologies to objectively measure and improve clinicians’ performance, with the ultimate goal of enhancing patient safety and improving clinical outcomes. Our projects have been funded by NASA, NIH, DoD, and NSF, leveraging machine learning and artificial intelligence to support high-performance clinical care in a variety of settings, including emergency medicine, critical care, surgery, space, and military medicine.
The Postdoctoral Fellowship in Medical AI prepares the next generation of researchers to bridge artificial intelligence and clinical care. Building AI that clinicians trust and use requires both technical depth and clinical insight, so the fellowship is open to candidates from two backgrounds who work side by side in interdisciplinary teams:
- Computational Track – for graduates of doctoral programs in Science, Technology, Engineering, and Mathematics (STEM) with a solid foundation in applying advanced ML/AI techniques. This track is ideal for early-career scientists seeking experience in the healthcare field and the opportunity to apply AI/ML and software engineering to improve patient care and safety. Prior experience in healthcare or biomedical research is required.
- Clinician-Scientist Track – for clinicians (M.D., D.O., PhD, or equivalent) with research experience and a strong interest in medical AI. This track is ideal for clinicians who want to lead the design, clinical validation, and implementation of AI tools while building hands-on data science and AI skills. This is a full-time research position with no clinical duties.
Fellows on both tracks are mentored by a multidisciplinary team of experts, gaining extensive knowledge and experience in diverse research areas including medicine, human factors, cognitive science, behavioral sciences, and aerospace and military research. Fellow follow an individualized development plan, with support for first-author publications, presentations at national and international conferences, and applications for independent fellowships and career development awards.
Responsibilities
All fellows will:
- Conduct research and development in the emerging field of medical AI, from study design through publication, in settings such as emergency medicine, critical care, surgery, space, and military medicine
- Work with large multi-source clinical datasets, including time-series physiological data, demographics, behavioral and psychological assessments, video, audio, electronic health records (EHR), and clinical performance outcomes
- Contribute to the development and evaluation of AI-based medical simulation applications and real-time clinical decision support systems
- Lead first-author manuscripts and conference presentations, and contribute to grant proposals
- Collaborate closely with clinicians, engineers, and scientists, and help mentor students and junior team members
Computational Track fellows will focus on:
- Designing and developing software architectures and data pipelines for AI-based solutions
- Training, fine-tuning, and evaluating ML/AI models, including neural networks, large language models, and computer vision models
- Integrating AI models with large clinical databases and deploying them in simulation and real-time clinical environments
Clinician-Scientist Track fellows will focus on:
- Identifying clinically meaningful problems and translating them into AI research questions and study designs
- Designing and conducting simulation-based and clinical studies, including writing IRB protocols and leading data collection
- Defining clinical ground truth, including expert annotation of video, audio, physiological, and EHR data
- Evaluating AI systems, including large language models, for clinical accuracy, safety, bias, and usability, and studying how clinicians interact with AI tools
- Building hands-on skills in data analysis, programming, and AI through mentored projects and available training opportunities
Qualifications
Applicants must meet the requirements for all applicants plus those for their chosen track. Preferred qualifications are not required; we encourage you to apply even if you do not meet all of them.
Required for all applicants:
- Doctoral degree (Ph.D., M.D., or equivalent) completed by the start date
- At least three peer-reviewed publications in which the candidate was a significant contributor, including at least one as lead author. Both journal articles and full-length peer-reviewed conference papers (e.g., NeurIPS, ICML, MICCAI, CHI, ML4H) count.
- Strong interest in a research career at the intersection of AI and medicine
- Excellent written and verbal communication skills and the ability to work effectively in multidisciplinary teams
Required for the Computational Track:
- Ph.D. in a STEM discipline (e.g., computer science, electrical or biomedical engineering, data science, statistics)
- Proficiency in Python and/or C++
- Demonstrated experience with AI/ML model training and evaluation, including neural networks and/or large language models
- Demonstrated experience with PyTorch and/or TensorFlow and/or scikit-learn
- Familiarity with signal processing and time series analysis
Preferred for the Computational Track:
- Prior experience applying AI/ML to medical or health data
- Experience with computer vision (e.g., convolutional neural networks) and/or vision-language models
- Familiarity with cloud computing services and APIs
- Experience with multimodal data (e.g., video, audio, and physiological signals) or real-time systems
Required for the Clinician-Scientist Track:
- M.D., D.O., Ph.D., or equivalent clinical degree
- Demonstrated clinical research experience, including study design, data collection, and statistical analysis
- Hands-on experience analyzing research data using R, Python, Stata, SAS, or a similar tool
- Demonstrated engagement with AI, data science, or digital health, such as a research project that used AI/ML methods, an evaluation of AI tools or large language models, or coursework or a certificate in data science, AI, or clinical informatics
Preferred for the Clinician-Scientist Track:
- Completed residency or other clinical training, especially in emergency medicine, critical care, anesthesiology, or surgery
- Advanced training in research methods, epidemiology, biostatistics, or informatics (e.g., MPH, MS, MMSc, or Ph.D.)
- Programming experience in Python, including use of ML libraries
- Experience writing IRB protocols, conducting prospective data collection, or doing healthcare simulation research
Salary and Benefits
The selected candidate will be appointed for two years, with the opportunity to renew the appointment for a third and/or fourth year pending performance review and funding. The salary range for this position is $71,750 to $73,544 annually. Benefits include comprehensive health insurance, optional dental, vision, and retirement plans, and reimbursement for conference travel related to fellowship projects. BWH is a Harvard-affiliated teaching hospital, and the research fellow will be eligible for an appointment at Harvard Medical School.
Additional Job Details (if applicable)
IMPORTANT – APPLICATION INSTRUCTIONS
Please do not apply through the Workday “Apply” button.
This position is being posted for informational purposes only. To be considered, please submit the following directly to the hiring manager by clicking the link below:
- Cover letter (up to two pages) stating your track (Computational or Clinician-Scientist), your research interests and relevant experience, and what you hope to accomplish during the fellowship
- Current CV, including a full list of publications
- Recent work sample (e.g., a first-author peer-reviewed manuscript or pre-print; Computational Track applicants are encouraged to also include GitHub code or a software demo)
- Candidates selected for an interview will be asked to submit two letters of recommendation
Please note: Applications submitted through Workday will not be forwarded to the hiring manager and may not be reviewed.
Remote Type
Hybrid Work Location
75 Francis Street EEO Statement:
2200 The Brigham and Women's Hospital, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. To ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Veteran’s Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact Human Resources at (857)-282-7642.Mass General Brigham Competency Framework
At Mass General Brigham, our competency framework defines what effective leadership “looks like” by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused, half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance, make hiring decisions, identify development needs, mobilize employees across our system, and establish a strong talent pipeline.
Salary Context
This $71K-$73K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 Brigham and Women's Hospital, Inc., 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. This role's midpoint ($72K) sits 66% below the category median. Disclosed range: $71K to $73K.
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.
Brigham and Women's Hospital, Inc. AI Hiring
Brigham and Women's Hospital, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $73K - $73K.
Location Context
AI roles in Boston pay a median of $215,000 across 386 tracked positions.
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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