Interested in this AI/ML Engineer role at Dana-Farber Cancer Institute?
Apply Now →About This Role
Job Ref:
JR\-5165
Location:
450 Brookline Ave, BOSTON, MA 02215
Category:
Operations
Employment Type:
Full time
Work Location:
Onsite: occasional remote
Salary/Pay Rate:
$247,600\.00 \- $284,033\.00 per year
Overview
The Senior Director, AI Innovations serves as a strategic leader within Dana\-Farber’s AI Innovation Center, responsible for advancing high\-priority AI\-related initiatives that support the Institute’s research, clinical, and operational priorities. Reporting to the AI Center Lead, this role develops and manages implementation of an integrated strategic plan for AI innovation and supports related business and operating plans across the Institute. The position works closely with executive leadership, faculty, and internal and external partners to assess opportunities, address risks, and align AI initiatives with institutional goals. This role requires strong strategic planning, program leadership, analytical, and stakeholder management capabilities in a complex academic healthcare and research environment.
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.
Primary Duties and Responsibilities:
- Leads development and implementation of the Institute’s AI innovation strategic plan in partnership with the AI Center Lead and executive leadership, identifying strategic priorities, dependencies, resource needs, and implementation requirements across research, clinical, and operational domains.
- Conducts ongoing assessment of the AI, biomedical research, and healthcare landscape to identify emerging opportunities, institutional strengths, competitive risks, and market considerations relevant to Dana\-Farber’s mission and long\-term positioning.
- Conduct requisite analyses and develop business and operating plans for priority AI and data science initiatives in collaboration with multiple Dana\-Farber offices.
- Provide project management assistance to ensure timely and successful execution of initiatives.
- Foster the establishment of appropriate AI governance and lifecycle management, including considerations of ongoing capability evaluation, risk analysis, and accountability
- Directs high\-priority AI innovation initiatives from planning through execution, establishing project plans, governance structures, milestones, and accountability measures to support timely and effective delivery.
- Develops business cases, operating models, and implementation plans for priority AI initiatives in collaboration with finance, research, clinical, digital, and administrative stakeholders across the Institute.
- Establishes metrics, dashboards, and reporting processes to evaluate initiative progress, outcomes, and organizational impact, and provides regular updates, analyses, and recommendations to leadership.
- Partners with faculty, institutional leaders, and external collaborators to align AI innovation efforts with strategic priorities, operational capabilities, research objectives, and evolving organizational needs.
- Collaborates with AI Center leadership to guide and support interdisciplinary teams responsible for implementing AI innovation priorities across multiple workstreams.
- Establish and manage strategic partnerships with industry leaders, academic institutions, and government agencies.
Knowledge, Skills and Abilities:
- Expert knowledge of current and emerging AI technologies, trends, and applications relevant to biomedical research, healthcare delivery, and administrative operations.
- Strong knowledge of strategic planning, research administration, business planning, market analysis, and enterprise program implementation in complex organizations.
- Ability to translate emerging AI concepts into practical institutional strategies, operating models, and implementation plans.
- Demonstrated ability to lead complex, cross\-functional initiatives in a highly matrixed academic healthcare or research environment.
- Strong analytical and critical thinking skills, including the ability to synthesize complex qualitative and quantitative information into actionable recommendations.
- Excellent project and program management skills, with the ability to manage multiple priorities, deadlines, dependencies, and stakeholders simultaneously.
- Ability to develop metrics, reporting frameworks, and performance monitoring tools to evaluate initiative success and support decision\-making.
- Strong interpersonal, written, and verbal communication skills, with the ability to influence senior leaders, faculty, and partners at all levels.
- Ability to build collaborative relationships across research, clinical, operational, and administrative functions.
- Strong judgment and adaptability in navigating ambiguity, organizational complexity, and evolving strategic priorities.
- Knowledge of governance, change management, and organizational adoption considerations associated with enterprise innovation initiatives.
- Commitment to fostering a collaborative, inclusive, and high\-performing work environment.
Minimum Job Qualifications:
Master’s degree in life sciences, biomedical sciences, public health, business administration, health administration, data science, informatics, or related field required; Ph.D. preferred.
- Minimum of 10 years of progressively responsible experience in strategic planning, research administration, innovation program leadership, or related management roles within a healthcare, academic medical center, research, or biomedical environment.
- Demonstrated success leading large, cross\-functional initiatives and translating strategy into operational execution required.
- Experience in oncology, biomedical research, digital health, artificial intelligence, or data science\-enabled transformation preferred.
Supervisory Responsibilities:
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).
$247,600\.00 \- $284,033\.00
Salary Context
This $247K-$284K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($265K) sits 44% above the category median. Disclosed range: $247K to $284K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; 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 $216,350 across 460 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.