Interested in this AI/ML Engineer role at Harvard University?
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June 05, 2026003009SR
Company Description
By working at Harvard University, you join a vibrant community that advances Harvard's world\-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.
Why join Harvard Business School?
Harvard Business School, located on a 40\-acre campus in Boston, was founded in 1908 as part of Harvard University. It is among the world's most trusted sources of management education and thought leadership. For more than a century, the School's faculty has combined a passion for teaching with rigorous research conducted alongside practitioners at world\-leading organizations to educate leaders who make a difference in the world. Through a dynamic ecosystem of research, learning, and entrepreneurship that includes MBA, Doctoral, Executive Education, and Online programs, as well as numerous initiatives, centers, institutes, and labs, Harvard Business School fosters bold new ideas and collaborative learning networks that shape the future of business.
Job Description
Job Summary:
The Director of Corporate Alliances plays a central role in advancing the mission of the HBS AI Institute by serving as the primary relationship lead for a portfolio of corporate and organizational partners. This role is responsible for ensuring that partner organizations derive meaningful, sustained value from their engagement with the Institute, while also informing and enriching the Institute’s research agenda through close collaboration with faculty and practice.
Operating within a leading academic environment, the Director, reporting to Principal Director, Alliances at HBSAI, works at the intersection of academia and industry, partnering closely with faculty, researchers, and administrative teams to translate cutting\-edge research into actionable insights. The Director serves as a trusted advisor to senior leaders, facilitating high\-impact engagements that reflect the rigor, independence, and mission of a university\-based research institute. The role combines elements of strategic partnership development, academic engagement, and program delivery, with a focus on long\-term relationship stewardship, intellectual exchange, and measurable impact.
Job\-Specific Responsibilities:
- Manage a portfolio of strategic partner organizations, serving as the primary point of contact and steward of the overall relationship.
- Develop and execute engagement plans that align partner priorities with HBS AI Institute research, faculty expertise, and programming.
- Ensure a high\-quality partner experience across the full lifecycle, including onboarding, ongoing engagement, and long\-term value realization.
- Translate faculty research into clear, relevant insights for practitioners, supporting partners in applying these insights to organizational challenges.
- Convene and facilitate interactions between partners and faculty, including briefings, workshops, and collaborative research opportunities.
- Navigate university structures and processes to effectively coordinate across stakeholders, including faculty, research centers, and administrative units.
- Lead regular partner reviews to assess engagement, share insights, and identify opportunities for deeper collaboration.
- Monitor partner engagement and feedback to inform continuous improvement and ensure alignment with Institute goals.
- Serve as a conduit between partner organizations and the Institute, elevating practice\-based insights to help shape research priorities and initiatives.
- Contribute to the development of new partnership opportunities through a thoughtful, relationship\-driven approach.
- Collaborate with colleagues across HBS, including Executive Education and HBS Online, to deliver integrated learning and engagement experiences.
- Support the evolution of the Institute’s partner engagement model, including defining best practices and success metrics.
- Lead, coach, and develop a team of 1–3 staff members, fostering a collaborative, high\-performing environment that supports strategic corporate partnership goals.
- Build trust and collaboration by being present on\-site and engaging directly with colleagues and various constituents.
- Other duties as assigned.
Qualifications
Basic Qualifications:
- Bachelor’s degree is required.
- 10\+ years of experience in consulting, strategic partnerships, customer success, higher education administration, or related advisory roles.
- Demonstrated ability to manage complex, enterprise\-level relationships and drive measurable outcomes in matrixed or academic organizations.
- Experience working with senior executives on strategy, transformation, or innovation initiatives.
- Experience collaborating with faculty, researchers, or academic leadership, or navigating university\-based environments and stakeholder models.
- Ability to synthesize complex ideas into clear, actionable insights for executive audiences while maintaining academic rigor.
- Strong interest in digital transformation, data, and AI.
Additional Qualifications and Skills:
- MBA or equivalent experience.
- Background in consulting, enterprise customer success, or higher education partnerships.
- Experience working within or alongside universities, research institutes, or executive education programs.
- Experience defining success metrics and leading data\-informed client or partner engagements.
- Familiarity with academic research processes, publication norms, and faculty engagement models.
- Demonstrated experience leading, coaching, and developing staff, with a commitment to fostering high\-performing and collaborative teams.
Additional Information
- Standard Hours/Schedule: 40 hours per week
- Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position
- Pre\-Employment Screening: Identity, Education
- Other Information:
+ This is a hybrid position which we consider to be a combination of remote and onsite work at our Boston, MA based campus. HBS expects all staff to be onsite a minimum of 3 days per week and departments provide onsite coverage Monday – Friday. Specific hours and days onsite will be determined by business needs and are subject to change with appropriate advanced notice.
+ We may conduct candidate interviews virtually (phone and/or via Zoom) and/or in\-person for this role.
+ A cover letter is required to be considered for this opportunity.
Work Format Details
This position has been determined by school or unit leaders that some of the duties and responsibilities can be effectively performed at a non\-Harvard location. The work schedule and location will be set by the department at its discretion and based upon operational needs. When not working at a Harvard or Harvard\-designated location, employees in hybrid positions must work in a Harvard registered state in compliance with the University’s Policy on Employment Outside of Massachusetts. Additional details will be discussed during the interview process. Certain visa types and funding sources may limit work location. Individuals must meet work location sponsorship requirements prior to employment.
Salary Grade and Ranges
This position is salary grade level 059\. Please visit Harvard's Salary Ranges to view the corresponding salary range and related information.
Benefits
Harvard offers a comprehensive benefits package that is designed to support a healthy work\-life balance and your physical, mental and financial wellbeing. Because here, you are what matters. Our benefits include, but are not limited to:
- Generous paid time off including parental leave
- Medical, dental, and vision health insurance coverage starting on day one
- Retirement plans with university contributions
- Wellbeing and mental health resources
- Support for families and caregivers
- Professional development opportunities including tuition assistance and reimbursement
- Commuter benefits, discounts and campus perks
Learn more about these and additional benefits on our Benefits \& Wellbeing Page.
EEO/Non\-Discrimination Commitment Statement
Harvard University is committed to equal opportunity and non\-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard's academic purposes.
Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university's non\-discrimination policy. Harvard's equal employment opportunity policy and non\-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.
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 Harvard University, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
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.
Harvard University AI Hiring
Harvard University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
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
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