Position Description

Harvard Business School will not offer visa sponsorship for this opportunity.

As a Senior Machine Learning Engineer on our GenAI applications team, you will help lead development of innovative generative AI products that address the needs of our constituents (students, alumni, faculty, researchers, staff, and community at large). This key technical leadership role requires hands-on expertise across the full machine learning lifecycle. In this role, you will collaborate with data scientists, product managers, and data engineers to operationalize machine learning models in production and manage the lifecycle of artificial intelligence algorithms on a variety of domains. You will develop and deploy novel approaches to optimize existing machine learning systems to maximize their business value.

You will also help us build and scale our GenAI application platform. This platform will be the hub within Harvard Business School (HBS) where GenAI application developers can share their data and code. As custodians of this platform, we intend to use the best practices in the field along with existing repositories to expedite the path from prototype for GenAI applications and unlock economies of scale. You will be highly influential in advancing our GenAI applications and guide teams towards impactful and ethical AI. We seek an expert who is eager to grow and disseminate GenAI model expertise across the organization.

• Architect, build, maintain, and improve new and existing 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.
• Establish reusable frameworks to streamline model building, deployment and monitoring. Incorporate comprehensive monitoring, logging, tracing, and alerting mechanisms.
• Build guardrails, compliance rules and oversight workflows into the GenAI application platform, such as establishing approval chains for model updates and staged rollout for production releases.
• Develop templates, guides and sandbox environments for easy onboarding of new contributors and experimentation with new techniques.
• Ensure development of user-facing applications in the GenAI application platform is easy and safe by enforcing rigorous validation testing before publishing user-generated models and implement a clear peer review process of applications.
• Use your entrepreneurial spirit to identify new opportunities to optimize business processes, improve consumer experiences, and prototype solutions to demonstrate value.
• Work closely with data scientists and analysts to create and deploy new product features online and in mobile apps.
• Contribute to and promote good software engineering practices across the team.
• Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code.
• Actively contribute to and re-use community best practices.
• Monitor, debug, track, and resolve production issues.
• Work with project managers to ensure that projects proceed on time and on budget.
• Collaborate with Technical Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact.
• Complete other responsibilities as assigned.

Basic Qualifications

• Minimum of seven years? post-secondary education or relevant work experience

Additional Qualifications and Skills

Other Required Qualifications:

• Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline desired.
• Minimum of five years? software development experience with Python and SQL.
• Minimum of three years? experience building pipelines to deploy NLP and deep learning models into production in a cloud environment.
• Minimum three years? experience using PyTorch, Tensorflow, or MXNet, along with optimizing code for GPU clusters.
• Experience with production RAG pipelines and agentic information retrieval and search systems, with the ability to write production level code.
• Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using Langchain and similar tools.
• Experience with various embedding models and setting up and tuning vector databases to improve performance of semantic search and retrieval systems.
• Understand the underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs.
• Experience with cloud computing platforms and tools - AWS

Additional/Desired Qualifications:

• Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications using tools such as NeMo.
• Experience working with a variety of relational SQL and NoSQL databases, big data tools: Hadoop, Spark, Kafka; a Linux environment; and at least one cloud provider solution (AWS, GCP, Azure).
• Knowledge of data pipeline and workflow management tools.
• Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation.

Additional Information

This role has the possibility of being remote or hybrid.
Remote work may be considered for individuals living at least 100-mile radius from campus and where all work will be completed within a state that Harvard is registered to do business in (CA - exempt roles only, CT, GA, IL, MA, MD, ME, NH, NJ, NY, RI, VA, VT or WA).
We consider hybrid to be a combination of remote and 3 days per week onsite work at our Boston, MA based campus.
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.
As part of our evaluation, candidates are required to complete a Take Home Assignment / Hacker Rank assessment after clearing Technical Recruiter Screen. This assignment will test your specific skills/knowledge areas relevant to the role

Harvard Business School will not offer visa sponsorship for this opportunity.

Culture of Inclusion: The work and well-being of HBS is profoundly strengthened by the diversity of our network and our differences in background, culture, national origin, religion, sexual orientation, and life experiences. Explore more about HBS work culture here https://www.hbs.edu/employment.

Benefits

We invite you to visit Harvard's Total Rewards website (https://hr.harvard.edu/totalrewards) to learn more about our outstanding benefits package, which may include:

• Paid Time Off: 3-4 weeks of accrued vacation time per year (3 weeks for support staff and 4 weeks for administrative/professional staff), 12 accrued sick days per year, 12.5 holidays plus a Winter Recess in December/January, 3 personal days per year (prorated based on date of hire), and up to 12 weeks of paid leave for new parents who are primary care givers.
• Health and Welfare: Comprehensive medical, dental, and vision benefits, disability and life insurance programs, along with voluntary benefits. Most coverage begins as of your start date.
• Work/Life and Wellness: Child and elder/adult care resources including on campus childcare centers, Employee Assistance Program, and wellness programs related to stress management, nutrition, meditation, and more.
• Retirement: University-funded retirement plan with contributions from 5% to 15% of eligible compensation, based on age and earnings with full vesting after 3 years of service.
• Tuition Assistance Program: Competitive program including $40 per class at the Harvard Extension School and reduced tuition through other participating Harvard graduate schools.
• Tuition Reimbursement: Program that provides 75% to 90% reimbursement up to $5,250 per calendar year for eligible courses taken at other accredited institutions.
• Professional Development: Programs and classes at little or no cost, including through the Harvard Center for Workplace Development and LinkedIn Learning.
• Commuting and Transportation: Various commuter options handled through the Parking Office, including discounted parking, half-priced public transportation passes and pre-tax transit passes, biking benefits, and more.
• Harvard Facilities Access, Discounts and Perks: Access to Harvard athletic and fitness facilities, libraries, campus events, credit union, and more, as well as discounts to various types of services (legal, financial, etc.) and cultural and leisure activities throughout metro-Boston.

Work Format

Hybrid (partially on-site, partially remote)


Commitment to Equity, Diversity, Inclusion, and Belonging
Harvard University views equity, diversity, inclusion, and belonging as the pathway to achieving inclusive excellence and fostering a campus culture where everyone can thrive. We strive to create a community that draws upon the widest possible pool of talent to unify excellence and diversity while fully embracing individuals from varied backgrounds, cultures, races, identities, life experiences, perspectives, beliefs, and values.

EEO Statement
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.

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