Job Description
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.
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.
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.
- Build guardrails, compliance rules, and oversight workflows into the GenAI application platform.
- Develop templates, guides, and sandbox environments to support onboarding of new contributors.
- Ensure user-facing applications built on the GenAI application platform are safe and reliable.
- Apply an entrepreneurial mindset to identify opportunities to optimize business processes.
- Work closely with data scientists and analysts to develop and deploy new product features.
- Contribute to and promote sound software engineering practices across the team.
- Mentor and educate team members to adopt best practices.
- 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.
- Build trust and collaboration by being present on-site.
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).
- 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.
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