Interested in this AI/ML Engineer role at Dana-Farber Cancer Institute?
Apply Now →Skills & Technologies
About This Role
Job Ref:
JR\-4749
Location:
450 Brookline Ave, BOSTON, MA 02215
Category:
Operations
Employment Type:
Full time
Work Location:
Remote: 100% off site
Salary/Pay Rate:
$243,000\.00 \- $257,400\.00 per year
Overview
Informatics \& Analytics (I\&A) serves patients present and future, by collaboratively building a sustainable informatics and analytics ecosystem of tools and services to support and grow the research, clinical, and business missions of Dana\-Farber Cancer Institute (DFCI). The Senior Director of AI Operations and Data Science Services is an executive leader, serving as supervisor and mentor to 20\-35 FTEs, who is responsible for designing and managing Artificial Intelligence (AI)\-related tools, platforms, and services for 2000\+ DFCI users, for partnering with other DFCI leaders to deliver high\-impact AI solutions for clients across Research, Clinical, and Operations, and for providing leadership and expertise for the design of policies, processes, and governance for responsible and effective use of AI.
Located in Boston and the surrounding communities, Dana\-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high\-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School\-affiliated hospitals.
- Serves as the primary subject matter expert for AI technology, platforms, and services for the of department and Institute
- Responsible for architecting, building, and configuring Dana\-Farber’s operational AI tools and platforms
- Collaboratively develops institutional AI strategy in partnership with Research, Clinical, and Operational leaders
- Technical “horizontal” leader within I\&A for AI and data science use cases in Operations, Clinical Practice, and Research in partnership with the 3 “vertical” client\-facing leaders
- Manages $5M\+ of contracts and personnel resources (e.g, Snowflake, Databricks, John Snow Labs, Informatics Core Services, Azure, Onyx)
- Core Member of the Dana\-Farber AI Governance Committee
- Develops Institute AI policies and guidance for validation, testing, deployment, and monitoring of clinical artificial intelligence tools/models in partnership with the CIO, CQO, CDAO, General Counsel, and AI Governance Committee. Helps DFCI responds to regulatory guidelines from federal and state agencies
- Delivers $2\-5M in annual value from AI initiatives in partnership with I\&A client\-facing verticals
- Responsible for building the AI infrastructure necessary to support the $40M\+ federated AI research collaboration known as the Cancer AI Alliance, in partnership with other technical leaders and architects at Memorial Sloan Kettering, Fred Hutch Cancer Center, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, and Dana\-Farber
- Keeps abreast of AI \& data science strategies in other academic medical centers. Represents Dana\-Farber in conversations with AI leaders from other Cancer Centers and with AI/cloud vendors (e.g., Microsoft, Amazon, Google Cloud, NVIDIA)
- Responsible for developing and maintaining direct productive relationships with VPs and SVPs, including the CIO, Chief Counsel, CQO, CMIO, VP Enterprise Data \& Analytics, and VP Research Informatics Operations
- Interacts directly with research faculty to provide AI operations and data science services through the Informatics Core. Represents all Informatics \& Analytics capabilities to these clients
- Budget responsibility $5\-10M. Acts as a responsible steward of DFCI and I\&A financial resources. Responsible for managing an AI and data\-science core services team in support of DFCI research labs, which includes negotiation of service level agreements, and careful budget management
- Helps Dana\-Farber develop and advance our culture of measurement and learning, by partnering with the CQO, CMIO, research leaders, and others to design and implement quality\-improvement trials, randomization within the medical record, and other real\-world observational learning to improve the outcomes and experience of patients and providers
KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:
- Expert in the application of AI, data science, and machine learning in research and healthcare environments
- Machine Learning \& Deep Learning: Expertise in supervised and unsupervised learning, neural networks, NLP and LLM models for healthcare applications like predictive analytics, imaging, pathology, and clinical decision support
- Proficient in AI Frameworks \& Cloud Services, able to configure and deploy secure, scalable, HIPAA\-compliant AI solutions in commercial cloud vendors (e.g., Azure, Google Cloud) and in on\-premises high\-performance computing environments
- MLOps: Expertise in managing the model lifecycle (training, deployment, monitoring), using Kubernetes and orchestration systems as well as tools like MLFlow for assuring the reproducibility of computational research while ensuring compliance with relevant regulation (e.g., HIPAA) and applicable policies
- Experience with software licensing, copyright law, and patents
- Experience designing and deploying scalable AI systems for healthcare, preferably including radiology, digital pathology, medical text, audio recordings, claims data
- Experience using common healthcare data standards (e.g., DICOM, OMOP, FHIR, HL7\)
- Experience collaboratively developing AI strategy for an organization, preferably healthcare
- Experience leading multi\-disciplinary teams (ML \& data scientists, AI scientists \& engineers, data engineers, prompt engineers, clinicians) to collaboratively deliver high\-impact business initiatives
- Proven record of hiring top technical talent and ensuring growth and job satisfaction of team members. Able to build relationships with top graduate programs in the Boston area to develop talent pipelines
- Proficient in guiding and supporting career development and performance management, and developing new leaders
- Strong publication record in AI and biomedicine
- Experience developing AI policy and guidance for an institution, and ensuring deployed AI models meet scientific and safety standards, comply with healthcare regulations, and make a measurable difference
- Experience in turning AI models into consumable healthcare products and services, such as diagnostic tools or clinical decision support systems
- Clinical Collaboration: experience partnering with clinicians and other healthcare professionals to ensure AI tools address real\-world challenges and are validated for practical use in clinical settings
- Strong client service orientation with excellent interpersonal skills; able to interact effectively with all levels of staff and external contacts
- Presents results persuasively and accurately verbally or in writing, and in delivering good or bad news
- Collaborates effectively across the enterprise. Skillfully influences stakeholders to ask better questions and sharpen focus on analyses that will have the highest and broadest impact for DFCI
- Demonstrates self\-awareness, professionalism, agility and flexibility, a strong work ethic, appropriate humility, and the ability to lead through challenging situations. Thinks and acts consistently with a broader “we” mentality, i.e., what’s best for DFCI as a whole vs. self, “my team,” or “my group”, or “my department”
MINIMUM JOB QUALIFICATIONS:
The position requires a PhD in a STEM field, with 15\+ years of relevant professional experience, including 5\+ years in a biomedical or healthcare setting, and 10\+ years of direct people management experience. An advanced degree may substitute for some professional experience (master’s for 1 year; PhD for 3 years).
SUPERVISORY RESPONSIBILITIES:
Manages a group of 20\-35\+ FTE or budget equivalent, which includes other people managers. The senor director manages multiple teams. May supervise up to 50 additional FTE via matrix or vendor partnerships, including outsourced and offshore resources.
PATIENT CONTACT: None
At Dana\-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are committed to having faculty and staff who offer multifaceted experiences. Cancer knows no boundaries and when it comes to hiring the most dedicated and compassionate professionals, neither do we. If working in this kind of organization inspires you, we encourage you to .
Dana\-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.
### EEO Poster
.
Pay Transparency Statement
The hiring range is based on market pay structures, with individual salaries determined by factors such as business needs, market conditions, internal equity, and based on the candidate’s relevant experience, skills and qualifications.
For union positions, the pay range is determined by the Collective Bargaining Agreement (CBA).
$243,000\.00 \- $257,400\.00
Salary Context
This $243K-$257K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Dana-Farber Cancer Institute, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($250K) sits 38% above the category median. Disclosed range: $243K to $257K.
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.
Dana-Farber Cancer Institute AI Hiring
Dana-Farber Cancer Institute has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $257K - $284K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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 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).
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.