Machine Learning and Generative AI Engineer, Digital Transformation

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.

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.

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.

• 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.

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.