Machine Learning and Generative AI Engineer, Digital Transformation

Join the Harvard Business School Digital Transformation team as a Machine Learning and Generative AI Engineer. You will help lead the development of innovative generative AI products that address the needs of our constituents. This key technical leadership role requires hands-on expertise across the full machine learning and AI lifecycle.

What You Will Do

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.

Why It Might Be a Fit

We seek an expert eager to grow and disseminate GenAI expertise across the organization. You will play a central role in building and scaling our core application platform and advancing our GenAI capabilities.

Requirements

• Minimum of five years’ post-secondary education or relevant work experience
• Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline
• 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

Benefits

• 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