Machine Learning Engineer, Factset

• You will be working on a team in a fast-paced environment where you will have the opportunity to influence the design and architecture of our products
• An ideal candidate for the role would be an individual that has experience or a strong interest in working with data pipelines for unstructured data, a history of success deploying machine learning models to production, an interest in knowledge graph and semantic web technologies, and a passion for experimentation
• 3-5 years of software engineering experience with significant focus on AI/ML solutions in production environments
• Demonstrated expertise in cloud architecture, primarily AWS, with familiarity across a broad range of services
• Strong understanding of Natural Language Processing, Machine Learning, and Deep Learning fundamentals with proven history of successful model development and deployment
• Proficiency in Python, API development, and software engineering paradigms
• Excellent communication abilities, capable of engaging both technical and business audiences and leading cross-functional projects
• Knowledge of major database architectures including SQL, NoSQL, and Vector databases

Desirable

• Join FactSet's Data Solutions AI team where our mission is to transform how financial professionals discover and interact with data through cutting-edge AI and machine learning technologies
• We're building intelligent systems that enable clients to ask natural language questions about financial data and receive actionable insights powered by our semantic enrichment platform
• At the core of our infrastructure is a knowledge graph that connects financial concepts to FactSet's comprehensive structured and unstructured data offerings, powering innovative solutions that push the boundaries of FactSet’s products
• Scale our unstructured financial document enrichment pipeline that powers FactSet’s knowledge graph to support the ingestion and enrichment of 500,000+ news, transcripts, and filings document chunks per day across dozens of financial domains
• Enhance and scale our AWS-based infrastructure to ensure the efficient, reliable delivery of ML and AI projects, including the integration of agentic solutions
• Work closely with other engineers and product developers to integrate and manage diverse domains of ML and NLP models. Offer expert advice on model selection and deployment strategies
• In collaboration with agentic tooling (Claude Code, Cursor, CoPilot), manage the entire software development lifecycle, from the initial design and coding through to testing and the deployment of applications
• Act as a mentor to team members, promoting a culture of innovation and continuous learning within the team