Machine Learning and Generative AI Engineer, Digital Transformation

Harvard University; Massachusetts; Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Generous paid time off

Medical, dental, vision coverage

Retirement plans

Tuition assistance and reimbursement

Commuter benefits

Job summary

Harvard University is seeking a Machine Learning and Generative AI Engineer to lead GenAI product development across web and mobile platforms. You will operationalize models, optimize pipelines, and implement guardrails while collaborating with data scientists, PMs, and engineers.

The role emphasizes responsible AI, production-grade ML, and mentorship, with hybrid work at the Boston campus and on-site collaboration requirements.

Qualifications

• Bachelor’s degree in mathematics, CS, engineering, or similar field; or equivalent experience.
• 5+ years post-secondary education or work experience in ML/AI roles.
• 2–3 years developing NLP/deep learning models in cloud environments.
• Experience with PyTorch/TensorFlow and optimizing for GPUs.
• Experience building GenAI workflows (RAG, model chaining, dynamic prompting).
• Knowledge of model guardrails, bias mitigation, and semantic search tooling.
• Proficiency with relational/NoSQL DBs, Linux, and at least one cloud provider (AWS/GCP/Azure).
• Familiarity with data pipelines (Airflow/Prefect/Step Functions) and CI/CD.

Responsibilities

• Architect, build, and improve GenAI applications and their underlying systems.
• Automate ML pipelines, monitor performance, and optimize costs.
• Establish reusable frameworks for model building, deployment, and monitoring.
• Implement guardrails, bias mitigation, and approval workflows for production.
• Develop templates and sandbox environments to onboard contributors and experiment.
• Ensure safety and reliability of user-facing GenAI apps with robust testing.
• Collaborate with data scientists, PMs, and data engineers to ship features.
• Mentor team members on production-grade ML code and best practices.
• Contribute to open-source resources and community best practices.
• Monitor, debug, and resolve production issues; manage stakeholder expectations.

Skills

Python

SQL

NLP

Deep learning

PyTorch

TensorFlow

RAG

PEFT/SFT

LangChain

Vector databases

Education

Bachelor’s degree in a technical discipline

Tools

LangChain

LangGraph

Qdrant

Pinecone

Weaviate

Job description

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.

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

• 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.

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.