Quantitative Developer

Job Description Summary

Seated within our Quantitative Investment Science group, this position 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

What you will do:

* Build and product ionize 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 explain model behavior, data limitations, quality tradeoffs, and operational risk to technical and non-technical partners

* Familiarity with cloud platforms, containerized development, CI/CD, observability, and secure production deployment patterns

* Preferred: experience with financial data, time series data, private markets workflows, vector databases,…