"Senior Machine Learning Engineer, Generative AI Products, HBS Foundry"

Job Summary

Lead comprehensive applications/web development for highly complex projects; typically work as part of a team to implement complex business solutions. Deliver strategic and expert coding; focus on overarching development strategy for a large, complex, multi-faceted application. May manage a number of projects simultaneously.

Job-Specific Responsibilities

• Build trust and collaboration by being present on-site and engaging directly with colleagues and various constituents.
• 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.

Qualifications

Basic Qualifications

• Minimum of seven 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 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 building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using Langchain and similar tools
• Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications using tools such as NeMo
• 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 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

• Appointment End Date: This position is approved as a term appointment with an end date of June 30, 2027. There is a possibility of renewal/extension.
• 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
  • As part of our evaluation, candidates are required to complete a Take Home Assignment after clearing Technical Recruiter Screen. This assignment will test your specific skills/knowledge areas relevant to the 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 059. 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.