Interested in this AI/ML Engineer role at Harvard University?
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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 University Central Administration?
Harvard University's Central Administration (CADM) is a 5,000+ employee organization that supports the university's overall excellence by understanding and serving the needs of its schools, students, faculty, staff, alumni, and surrounding communities. Through dynamic and collaborative partnerships, CADM provides high-quality and efficient services to the schools to help them achieve their goals.
Harvard University Information Technology (HUIT) is a community of Information Technology professionals committed to understanding our users and devoted to making it easier for faculty, students, and staff to teach, research, learn, and work through the effective use of information technology. We are recruiting an IT workforce that has both breadth in their ability to collaborate and innovate across disciplines – and depth in specific areas of expertise. HUIT offers opportunities for IT professionals to learn and work in a unique technology landscape and service-focused environment. If you are a technically proficient, nimble, user-focused and accountable IT professional who also connects with the importance of collaborating well in a team environment we are looking for you!
More About HUIT:
Our Mission: huit.harvard.edu/about
We empower the Harvard community with essential and transformative technologies to advance education, knowledge, and discovery.
HUIT’s core values are:
- Human-centered
- University-focused
- Innovation-driven
- Team-oriented
IT Academy (designed for IT Staff):
HUIT’s IT Academy aims to enable each IT staff person to grow professionally and become a trusted partner to her or his team. The IT Academy is built on the belief that every IT staff member across the University (including technology employees at each school and campus) can grow in her or his area of expertise as well as building strong people and project management skills. Learn more here: https://itacademy.harvard.edu/.
Job Description
The AI Solutions Architect serves as a critical link between the FAS community and the rapidly evolving landscape of AI capabilities. The Architect leads opportunity discovery, problem definition, stakeholder alignment, and overall solution architecture. Working as a paired team, both the Architect and AI Solutions Engineer contribute hands-on from prototype through product delivery. The Architect contributes to implementation, testing, demonstrations, and production transition; the Engineer holds primary responsibility for detailed engineering, deployment, reliability, and operational readiness. The Architect ensures a clear path to secure, sustainable support.
Job-specific Responsibilities:
- Engage faculty, researchers, and staff across FAS to identify and frame AI opportunities and define intended outcomes.
- Lead requirements and tool selection, and design solutions per established patterns and University data, security, and privacy policy.
- Scope problem statements and lead rapid prototyping (AI platforms, APIs, low-code/no-code), partnering with the Engineer to put working software in front of stakeholders early.
- Assess sustainability - including cost, security, maintenance, service-owner capacity - and define readiness and acceptance criteria..
- Own the governance relationship (security, privacy, accessibility, data-governance, research computing) and secure the approvals that turn prototypes into supported services.
- Communicate across all levels of technical fluency and lead stakeholder alignment throughout delivery.
- Document architectures, decisions, and trade-offs so others can maintain and extend the work.
- Track the evolving AI landscape and lead evidence-based recommendations on readiness, capability, risk, cost, and fit.
- Contribute hands-on to implementation, testing, demos, and production transition, with the Engineer primary on detailed engineering and operations.
- Partner with HUIT's AI Innovation team to evaluate emerging capabilities and lead user-value prototyping.
Qualifications
Basic Qualifications:
Basic Qualifications are the minimum threshold a candidate must meet in order to be considered for this role.
- Minimum of seven years’ post-secondary education or relevant work experience
Additional Qualifications and Skills:
The following qualifications are strongly preferred. If you meet some, but not all, you are still encouraged to apply; we value employees with a willingness to learn.
- 5+ years of experience in software development or solutions architecture
- 2+ years of hands-on experience with AI/ML platforms, LLMs, or applied AI tooling
- Proficiency with AI/ML frameworks and APIs, including LLM integration, retrieval/grounding, prompt design, and evaluation
- Experience rapidly prototyping with low-code/no-code and full-stack tools
- Strong communication across technical and non-technical audiences
- Systems thinking focused on privacy, integration, and maintainability
- Strong analytical reasoning and systems thinking
- Excellent writing and communication skills, including synthesizing complex, technical information for both technical and executive audiences
- Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent experience
Additional Information
- Hybrid: This position operates in a standard hybrid office/remote environment. Occasional on-campus presence may be required to support production systems and collaborate with HUIT and FAS teams.
- Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position
- Pre-Employment Screening: Identity
- Other Information:
Please provide a cover letter and resume as one document with your application.
This position has an 180-day orientation and review period.
#LI-BT1
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 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 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 $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
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 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
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