The RoleAs the Staff Machine Learning Engineer (VP) on the AI Science team, you will be responsible for designing and deploying production AI systems that power investment banking and capital markets workflows across the enterprise. Reporting to the Executive Director of AI, you will play a critical role in building AI-powered products that deliver measurable business outcomes for senior stakeholders including Managing Directors and portfolio managers.
You will bridge AI engineering and capital markets, combining deep understanding of investment banking valuation, fixed income markets, and credit analysis with hands-on ability to build and ship LLM-powered products at speed. You will have direct visibility to senior business stakeholders, translating complex business workflows into working AI systems.
This is a hands-on individual contributor role: you will spend the majority of your time designing, building, and shipping systems, while acting as a technical thought leader who helps shape the direction of the organization's AI investments and fosters a culture of rapid iteration, rigorous evaluation, and responsible AI.
Key Responsibilities:
- Design and deploy AI systems that automate high-impact investment banking and capital markets workflows-compressing multi-hour analytical tasks into minutes while meeting the accuracy, auditability, and reliability standards of front-office users.
- Build and own production LLM pipelines end-to-end: structured extraction from complex financial documents, retrieval-augmented generation, multi-step orchestration, and structured output parsing.
- Evaluate and champion emerging AI techniques and tools (e.g., agentic workflows, LLM evaluation frameworks, vector databases, RAG architectures) through hands-on prototyping, benchmarking, and iterative deployment.
- Partner with AI researchers, data scientists, and domain experts to translate experimental models into production-ready systems-hardening prototypes for latency, cost, accuracy, and reliability while generalizing solutions across multiple business domains.
- Own the development of reusable AI capabilities and platform components that serve as building blocks for downstream applications across the organization, setting the engineering standards for how AI systems are built, evaluated, and maintained.
- Collaborate directly with senior business stakeholders (Managing Directors, portfolio managers, research analysts) to understand workflows, gather feedback, and iterate on AI products that meet practitioner-grade quality standards.
- Build AI-driven analytics for fixed income and credit markets that fuse quantitative signals with unstructured data-turning market data, filings, and research into decision-ready insight for investment professionals.
- Mentor engineers and data scientists through design reviews, code reviews, and hands-on pairing-raising the bar for technical excellence and engineering rigor across the team.
RequirementsQualifications:
- 8+ years of experience building and deploying software or ML systems in production environments, with significant experience in financial services-preferably investment banking, asset management, fixed income, or credit markets.
- Proven track record of leading AI/ML projects from ideation to production, including cross-functional collaboration, technical ownership, and direct engagement with business stakeholders.
- Hands-on experience building production LLM applications: prompt engineering, orchestration (e.g., multi-agent systems, chained workflows), retrieval-augmented generation, structured output parsing, and evaluation pipelines.
- Deep expertise in at least one of: supervised/unsupervised learning, statistical modeling, NLP, or information extraction from unstructured documents.
- Strong foundation in investment banking and capital markets concepts-including valuation methodologies (DCF, comps, precedent transactions), credit analysis, fixed income analytics, and financial statement interpretation.
- Proficiency in Python, along with modern AI/ML tools (e.g., PyTorch, scikit-learn, LangChain/LangGraph, vector databases, LLM APIs), with working knowledge of MLOps practices (CI/CD, model monitoring, evaluation) and cloud infrastructure (AWS, GCP, or similar).
- Exceptional communication and collaboration skills, with the ability to translate technical details into strategic decisions and present directly to senior financial services stakeholders.
- Master's or PhD in Computer Science, Machine Learning, Statistics, Financial Engineering, or a closely related discipline preferred.
Preferred Qualifications:
- Hands-on experience with enterprise data and AI platforms (e.g., Palantir Foundry or similar)-including developing, deploying, and integrating AI solutions within an integrated data ecosystem.
- CFA or FRM certification.
- Prior experience in a client-facing capacity within financial services.
BenefitsPosition Location This is a hybrid position based out of our New York, NY office.CompensationThe base pay for this position is $290,000-300,000. A bonus will be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits.
