Machine Learning Engineer - Python, SQL, NoSQL, and Vector databases
Role details
Job location
Tech stack
Job description
- 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.
Note : Although we utilize agentic tools extensively at FactSet (Claude Code, Cursor, GitHub Copilot), we expect candidates for this role to demonstrate strong software engineering and machine learning fundamentals during the interview process.
Requirements
Do you have experience in Software deployment?, Do you have a Master's degree?, 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.
Desired Skills
- Experience with Knowledge Graphs and architecting LLM-powered solutions.
- Familiarity with financial data, applications, and specific industry challenges.
- Expertise in NLP libraries such as nltk and SpaCy and proficiency in unstructured text analysis.
- Demonstrable leadership capabilities and experience in mentoring or leading a team.
Benefits & conditions
At FactSet, our people are our greatest asset, and our culture is our biggest competitive advantage. Being a FactSetter means:
- The opportunity to join an S&P 500 company with over 45 years of sustainable growth powered by the entrepreneurial spirit of a start-up.
- Support for your total well-being. This includes health, life, and disability insurance, as well as retirement savings plans and a discounted employee stock purchase program, plus paid time off for holidays, family leave, and company-wide wellness days.
- Flexible work accommodations. We value work/life harmony and offer our employees a range of accommodations to help them achieve success both at work and in their personal lives.
- A global community dedicated to volunteerism and sustainability, where collaboration is always encouraged, and individuality drives solutions.
- Career progression planning with dedicated time each month for learning and development.
- Business Resource Groups open to all employees that serve as a catalyst for connection, growth, and belonging.
Salary is just one component of our compensation package and is based on several factors including but not limited to education, work experience, and certifications.
Use of AI tools during the interview process
The landscape of AI tools is rapidly evolving and FactSet engineers use AI tools extensively as a part of their daily work. During th You are encouraged to use AI tools like ChatGPT, Claude, Copilot when:
- Refining your resume and cover letter for submission
- Preparing for your interview, and researching FactSet and its products
You may not use AI tools:
- During an interview, or
- When explicitly requested not to use AI tools