Applied Research – Artificial Intelligence – Associate NY

Job Description What We Do

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action.

Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering, which is comprised of our Technology Division and global strategist groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.

Goldman Sachs is seeking an Applied AI Researcher to join our dynamic Applied Artificial Intelligence (AI) Research team. As an integral part of the team, you will play a pivotal role in driving the adoption of cutting‑edge AI and Machine Learning (ML) technologies at the firm.

In this role, you will have the opportunity to contribute to various AI/ML domains, including but not limited to machine learning, deep learning, natural language processing, information retrieval, time series analysis, and recommender systems. As an experienced AI Researcher, you will help address the unique challenges that arise in machine learning systems within the financial domain. Join us in redefining the boundaries of what’s possible in the intersection of quantitative research and artificial intelligence!

• Collaborating effectively with colleagues to advance production machine‑learning systems and applications.
• Conceptualizing, experimenting with, and assessing AI/ML‑based software systems.
• Developing, testing, and maintaining high‑quality, production‑ready code.
• Demonstrating technical leadership by taking charge of cross‑team projects.
• Creating libraries and frameworks that underpin reliable and testable systems.
• Represent Goldman Sachs at conferences and within open‑source communities.

• A Master’s or Ph.D. degree in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, Quantitative Finance, or equivalent relevant industry experience.
• A minimum of 1‑3 years of AI/ML experience in the industry that demonstrates your expertise.
• Extensive experience in software development for quantitative investment workflows in equities, fixed income, or multi‑asset strategies.
• Proficiency in contemporary programming languages like Python, C, or Java.

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We’re committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Learn more about our culture, benefits, and people at

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more:

The expected base salary for this New York, New York, United States‑based position is $115,000‑$180,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year‑end.

Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non‑temporary, full‑time and part‑time US employees who work at least 20 hours per week, can be found here.