Interested in this AI/ML Engineer role at Harvard University?
Apply Now →Skills & Technologies
About This Role
July 01, 2026003173SR
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 (HBS), 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
Be a pioneer in business, education, and global impact by joining the Harvard Business School Digital Transformation team \- a “startup with assets,” where you will have the chance to deploy cutting\-edge digital and emerging\-technology education solutions. *Where else can you make a difference at the intersection of cutting\-edge technology, world\-class education, noble purpose, and timeless legacy?*
As a Machine Learning and Generative AI Engineer on our team, you will help lead the development of innovative generative AI products that address the needs of our constituents (students, alumni, faculty, researchers, staff, and the community at large). This key technical leadership role requires hands\-on expertise across the full machine learning and AI lifecycle. You will collaborate with data scientists, product managers, and data engineers to operationalize AI models in production, drive core platform capabilities, and apply these in a variety of domains. You will also develop and deploy novel approaches to optimize existing AI systems and maximize their business value.
You will play a central role in building and scaling our core application platform — the hub within HBS where application developers can share data and code. As custodians of this platform, we will apply best practices and leverage existing repositories to accelerate the path from prototype for GenAI applications and unlock economies of scale. You will be highly influential in advancing our GenAI capabilities, guiding the teams towards impactful and ethical AI. We seek an expert eager to grow and disseminate GenAI expertise across the organization.
Duties and Responsibilities:
- Architect, build, maintain, and improve a suite of GenAI applications and their underlying systems.
- Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA and other parameter\-efficient methods.
- Establish reusable frameworks to streamline model building, deployment and monitoring. Incorporate comprehensive logging, tracing, and alerting mechanisms.
- Build guardrails, compliance rules, and oversight workflows into the GenAI application platform, including approval chains for model updates and staged rollouts for production releases.
- Develop templates, guides, and sandbox environments to support onboarding of new contributors and experimentation with emerging techniques
- Ensure user\-facing applications built on the GenAI application platform are safe and reliable, enforcing rigorous validation and testing before publishing, and implement a clear peer review process.
- Apply an entrepreneurial mindset to identify opportunities to optimize business processes, improve user experiences, and prototype solutions that demonstrate value.
- Work closely with data scientists and analysts to develop and deploy new product features across web and mobile applications.
- Contribute to and promote sound software engineering practices across the team.
- Mentor and educate team members to adopt best practices in writing and maintaining production\-grade machine learning code.
- Actively contribute to and leverage community best practices and open\-source resources.
- Monitor, debug, and resolve production issues in a timely manner.
- Partner with project managers to ensure projects are delivered on time and within budget.
- Collaborate with Technical Product Managers to track algorithmic performance KPIs and prioritize performance improvements based on effort and impact.
- Build trust and collaboration by being present on\-site and engaging directly with colleagues and various constituents.
- Complete other responsibilities as assigned.
Qualifications
Basic Qualifications:
- Minimum of five years’ post\-secondary education or relevant work experience
Additional Qualifications and Skills:
- Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline desired
- Minimum of two to three years’ software development experience with Python and SQL.
- Minimum of two to three years of experience building and deploying NLP and deep learning model pipelines into a cloud environment.
- Minimum two to three years of experience using PyTorch or Tensorflow, including optimizing code for GPU clusters
- Experience building advanced GenAI workflows such as retrieval\-augmented generation (RAG), model chaining, dynamic prompting, and parameter\-efficient fine\-tuning (PEFT/SFT) using LangChain, LangGraph, or similar frameworks.
- Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications.
- Experience with embedding models and tuning vector databases (e.g., Qdrant, Pinecone, Weaviate) to improve semantic search and retrieval performance.
- Solid understanding of the theoretical foundations of LLMs, including Transformer architectures and self\-attention mechanisms.
- Experience with relational and NoSQL databases, big data tools (Spark, Kafka), Linux environments, and at least one major cloud provider (AWS, GCP, Azure).
- Familiarity with data pipeline and workflow management tools (e.g., Airflow, Prefect, or Step Functions).
- Strong software engineering fundamentals: unit testing, CI/CD, code reviews, and design documentation.
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, Criminal
- 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.
\#LI\-KR1
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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; 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 $210,000 across 97 tracked positions. That's 3% below 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.