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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 the Harvard Graduate School of Education?
The Harvard Graduate School of Education (HGSE) is a diverse community of learners, teachers, and employees who are passionate about changing the world through education and striving for maximum impact in the field of education.
Many choose to work at the Harvard Graduate School of Education because they believe in our mission and are excited by our vision for the future. We have a reputation as a great place to work, for our excellent leadership, and we are a strong community that values diversity. For more information about HGSE, its programs, research, and faculty, please visit: www.gse.harvard.edu.
Job Description Job Summary:
Two new initiatives at the Harvard Graduate School of Education (HGSE) jointly seek a full\-time Research Associate to support research, partnership development, and programmatic activities across two complementary efforts:
- The AI \& Learning Initiative, which advances an agenda around AI and human development, with a focus on the science of learning, development, and human thriving.
- The Workforce Learning and Artificial Intelligence Initiative (WLAI), which focuses on how adults learn and develop skills in and for work.
This role is split across each initiative (70% AI \& Learning, 30% WLAI)
Reporting to WLAI Faculty Director Dr. Tessa Forshaw, and working closely with the AI and Learning Initiative team, collaborators across HGSE, and external organizations, the Research Associate will operate at the intersection of learning science, workforce practice, and AI in education.
Job\-Specific Responsibilities: AI \& Learning Initiative, (70%)
AI\-Learning Tool Design, Development, and Iteration (\~30%)
- Support the design, development, and testing of AI\-learning tools, ensuring they are grounded in the science of learning, development, and human thriving.
- Help translate research and faculty expertise into tool features, content, and user experiences that are practical for educators, parents, policymakers, and technology builders.
- Maintain and update the Lab’s training corpus and underlying AI models, incorporating new content, research findings, and user feedback
- Coordinate and conduct user research and usability testing with educators, students, and other stakeholders to understand needs, pain points, and opportunities for improvement.
- Collect, analyze, and synthesize user feedback to inform iterative design cycles and revised versions of AI\-learning tools.
- Monitor emerging trends, practices, and questions in AI and education to ensure the lab’s tools remain responsive to the field’s evolving needs.
Research and Communications (\~20%)
- Conduct and synthesize research on AI in education, developmental considerations, and human–AI interaction in learning contexts.
- Support thought leadership activities (e.g., writing briefs, articles, and presentations) that help shape public and professional conversations about AI and learning.
- Contribute to external\-facing content (e.g., website copy, newsletters, slide decks, social media) to share the Lab’s tools, insights, and events with broader audiences.
Workforce Learning \& AI Initiative (30%)
Research (\~15%)
- Contribute to a focused research agenda on workplace learning and AI in workforce contexts, including literature reviews, synthesis, and applied writing (e.g., briefs, case studies).
- Support design\-based and mixed\-methods research including study design, protocol development, data collection, and IRB processes.
- Collect and analyze qualitative and quantitative data (e.g., qualitative coding, thematic analysis, descriptive statistics, and basic quantitative methods).
Program \& Partnership Development (\~15%)
- Support WLAI’s certificate program including coordinating the teaching team, preparing curriculum materials, and gathering feedback from participants and partners.
- Lead or support partnership development and management with employers, workforce boards, training providers, and other organizational partners, including:
+ Serve as day\-to\-day point of contact for the UNITAR partnership, coordinating scopes of work, timelines, and deliverables.
+ Identifying and cultivating new applied research and programmatic partners.
Qualifications Basic Qualifications:
- Bachelor's degree required
- Minimum 4 years of relevant experience (such as research, project/program coordination, consulting, learning and development, workforce development, product or tool development, or related work).
- Experience with basic quantitative methods (e.g., descriptive statistics, linear regression) and statistical software such as R or similar tools.
- Experience coordinating multi\-stakeholder projects or programs in education, workforce, or adjacent fields.
- Occasional early morning, evening, or weekend hours may be required to support events, partner meetings, and program activities.
Additional Qualifications and Skills:
- Master’s degree in a relevant field such as education, learning sciences, cognitive science, psychology, organizational behavior, public policy, human–computer interaction, or a related discipline. Equivalent professional experience in L\&D, consulting, workforce development, or adjacent fields will also be considered.
- Experience with design\-based research, design thinking, or user\-centered design approaches, particularly in educational or workforce settings.
- Demonstrated experience in partnership development and management, such as working with employers, workforce boards, education systems, or technology companies.
- Demonstrated interest in workplace learning, workforce development, adult skill acquisition, and/or the application of learning science to professional contexts.
- Demonstrated interest in or experience with AI in education or workforce contexts, including comfort learning and using AI tools across research, productivity, and/or educational applications.
- Initiative, adaptability, and comfort working in new and evolving initiatives where the work spans research, programming, partnerships, and communications.
- Excellent written and verbal communication skills, with the ability to tailor communication to academic, professional, technical, and practitioner audiences.
- Strong ability to work both independently and collaboratively in a fast\-paced, entrepreneurial environment.
- Experience maintaining datasets/corpora or fine\-tuning AI models is a plus
Additional Information
- Appointment End Date: One year from date of hire.
- Standard Hours/Schedule: 35 hours per week
- Compensation Range/Rate (including Shift Differential if applicable): $90,000 \- $95,000 commensurate with experience
- Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position
- Pre\-Employment Screening: Identity and Education
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 057\. 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.
Salary Context
This $90K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($92K) sits 57% below the category median. Disclosed range: $90K to $95K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Harvard University AI Hiring
Harvard University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $95K - $95K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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