Senior Machine Learning Engineer Generative AI Applications

Position Description

Harvard Business School will not offer visa sponsorship for this opportunity.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 Engineer, 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.Duties and Responsibilities:

• Architect, build, maintain, and improve new and existing suite of algorithms and their underlying systems.
• Automate machine learning pipelines and monitor and optimize machine learning solutions.
• Implement end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance tuning, and A/B testing.
• 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.
• Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation.
• Write efficient and well-organized software to ship products in an iterative, continual-release environment.
• 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:

• Minimum of seven 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 five years? software development experience with Python and SQL.
• Minimum of three years? experience in developing and deploying machine learning systems into production in a cloud environment.
• Minimum of two years? experience testing, maintaining, or launching software products and minimum of one year of experience with software design and architecture.
• 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.

Other Preferred Qualifications:

• Experience with neural networks, deep learning, and reinforcement learning, using frameworks such as TensorFlow.
• Experience with Natural Language Processing (NLP), Large Language Models (LLMs), and/or Recommendation Engines.
• Relevant working experience with Docker and Kubernetes.

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 roleHarvard 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 BelongingHarvard 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 StatementWe 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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