Interested in this AI/ML Engineer role at Harvard University?
Apply Now →Skills & Technologies
About This Role
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 AI Technical Product Manager bridges the gap between artificial intelligence capabilities and real-world product applications, combining deep technical understanding with strategic product understanding to build AI-powered solutions that deliver measurable value.
Job-Specific Responsibilities:
Product
- Balance innovation with practical implementation, assessing technical feasibility and business impact
- Establish success metrics and KPIs for AI product initiatives
Technical Leadership
- Collaborate with data scientists, ML engineers, and software developers to translate business requirements into technical specifications
- Understand AI/ML fundamentals including model architectures, training processes, evaluation metrics, and deployment considerations
- Make informed decisions about model selection, data requirements, and infrastructure needs
- Evaluate emerging AI technologies and determine their applicability to product challenges and understand risk mitigation strategies.
Cross-Functional Collaboration
- Partner with engineering teams to prioritize features and manage the development lifecycle
- Work with design teams to create intuitive user experiences that leverage AI capabilities effectively
- Coordinate with data engineering on data pipelines, quality, and governance
- Communicate technical concepts to non-technical stakeholders including executives and customers
Product Development & Execution
- Align with Project Director on strategic priorities, customer experience and usability needs, and internal / external deadlines.
- Own the product backlog, writing detailed user stories and acceptance criteria for AI features
- Manage tradeoffs between model performance, latency, cost, and user experience
- Oversee A/B testing and experimentation frameworks to validate AI-driven improvements
- Monitor model performance in production and coordinate retraining or optimization efforts
Ethics & Risk Management
- Ensure responsible AI practices including fairness, transparency, and privacy considerations
- Identify potential biases in training data and model outputs
- Establish governance frameworks for AI model deployment and monitoring
- Navigate regulatory requirements, security needs, and compliance considerations
- Build trust and collaboration by being present on-site and engaging directly with
colleagues and various constituents.
- This role is responsible for other duties as assigned
Qualifications
Basic Qualifications:
- Minimum of five years’ post-secondary education or relevant work experience
Technical Background
- Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field
- 5+ years of product management experience with 2+ years specifically on AI/ML products
- Strong understanding of machine learning concepts, algorithms, and deployment architectures
- Experience with AI/ML tools and frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
- Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices
Product Management Skills
- Proven track record of shipping successful AI-powered products from concept to launch
- Expertise in agile methodologies and product development frameworks
- Excellent stakeholder management and communication abilities
Domain Knowledge
- Understanding of AI applications in relevant industry verticals
- Knowledge of generative AI, NLP, computer vision, or other specialized AI domains as applicable
- Awareness of AI ethics, bias mitigation, and responsible AI principles
Additional Qualifications and Skills:
- Data science background with tech development experience
- Master's preferred
- Experience with large language models, prompt engineering, or RAG systems
- Background in software engineering or data science
- Track record of managing products at scale with millions of users
- Exposure navigating AI regulatory landscapes (EU AI Act, etc.)
Key Competencies
- Technical depth - Ability to engage credibly with AI/ML engineers on architecture and implementation details
- Strategic thinking - Seeing beyond immediate features to long-term product evolution as identified by Project Director
- Motivation and problem solving– empowering technical team to overcome real or perceived barriers to execute on time
- User understanding – Working with UX/UI team to translate complex AI capabilities into intuitive user experiences
- Data-driven decision making - Using metrics and experimentation to validate hypotheses
- Communication - Explaining technical concepts clearly to diverse audiences
- Adaptability - Thriving in the rapidly evolving AI landscape
This role requires someone who is equally comfortable discussing neural network architectures with engineers and working with business team members, serving as a crucial link between AI innovation and product success.
Additional Information
- Appointment End Date: This position is approved for a (2)-year term (with possibility of
renewal/extension) which begins on the person's first day of employment.
- 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 allstaff 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.
#LI-MM1
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 058. 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 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 $212,000 based on 11,866 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 $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Harvard University AI Hiring
Harvard University has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Cambridge, MA, US.
Location Context
AI roles in Boston pay a median of $215,000 across 323 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.