Build and productionize ML models, feature pipelines, and inference workflows for QIS applications
Develop semantic matching, ranking, recommendation, and peer-selection systems for funds, managers, deals, companies, and comparable opportunities
Build unstructured data intelligence, classification, enrichment, and AI-assisted review workflows for complex internal materials and operational datasets
Design agentic AI workflows that can plan multi-step analyses, call internal tools, retrieve relevant context, and produce traceable recommendations for human review
Create evaluation frameworks for AI agents, including task success metrics, regression suites, prompt/version tracking, guardrail tests, and failure-mode analysis
Create model evaluation harnesses, benchmark datasets, backtests, monitoring, drift detection, and quality gates so ML outputs can be measured and trusted
Integrate embeddings, retrieval, model-serving APIs, agent orchestration, batch jobs, and human-in-the-loop review controls into existing QIS tools
Partner with data and platform engineers to make ML workflows repeatable, observable, secure, and easy to operate
Establish practical MLOps patterns for experiment tracking, model versioning, deployment, rollback, audit trails, and production support
Translate investment workflow needs into pragmatic ML solutions while being clear about limitations, confidence, and operational risk
What you bring:
Strong proficiency in Python and modern software engineering practices
Experience with applied machine learning, including feature engineering, model training, evaluation, inference, and monitoring
Ability to learn and apply the right ML, statistical, and data engineering tools for the problem, with sound judgment around model choice, data representation, reproducibility, and production constraints
Strong SQL skills and comfort designing data models for analytical or product-facing systems
Experience building production services, APIs, batch jobs, queues, or scheduled pipelines around data-intensive workflows
Practical experience with embeddings, semantic search, ranking, recommendation systems, information extraction, agentic AI systems, or LLM-enabled workflows
Familiarity with agent patterns such as tool use, retrieval-augmented generation, planning, memory, workflow orchestration, and structured human review
Strong testing habits and ability to debug model behavior using real data, logs, metrics, and user feedback
Ability to ex
Benefits
Health insuranceRemote work options
Additional Information
Job Description Summary
For over forty years, HarbourVest has been home to a committed team of professionals with an entrepreneurial spirit and a desire to deliver impactful solutions to our clients and investing partners. As our global firm grows, we continue to add individuals who seek a collaborative, open-door culture that values diversity and innovative thinking.
In our collegial environment that's marked by low turnover and high energy, you'll be inspired to grow and thrive. Here, you will be encouraged to build on your strengths and acquire new skills and experiences.
We are committed to fostering an environment of inclusion that promotes mutual respect among all employees. Understanding and valuing these differences optimizes the potential of both the individual and the firm.
HarbourVest is an equal opportunity employer.
This position will be a hybrid work arrangement. You will receive 18 remote workdays per quarter to use at your discretion, subject to manager approval. For example, you may choose to work in the office 4 days per week and take one remote day weekly (typically 13 weeks per quarter), leaving 5 additional remote days to be used as needed.
Seated within our Quantitative Investment Science group, the Associate, Quantitative Developer turns machine learning, applied AI, and agentic workflow capabilities into reliable investment workflow software. This is a software engineering role first: you will write production Python, work deeply with data, build model pipelines and evaluation frameworks, and integrate AI-driven capabilities into the tools investment teams use every day. The role is ideal for a practical machine learning engineer who wants to build trusted, auditable systems for high-value quantitative and private markets workflows.
The ideal candidate is someone who has:
Strong software engineering fundamentals and a production-oriented machine learning mindset
A practical interest in using ML and agentic AI to improve investment research, data quality, decision support, and workflow scale
Healthy skepticism about model outputs, with strong instincts for evaluation, backtesting, monitoring, and human review
Comfort turning ambiguous analytical workflows into measurable, maintainable production systems
Strong collaboration skills across quant developers, data engineering, product, and investment stakeholders
Curiosity about finance, private markets, and the data problems behind investment decision-making
Harbourvest · Boston