Applied AI Engineer - Associate
@ Goldman Sachs

New York, NY
$150,000
On Site
Full Time
Posted 16 hours ago

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XXXXXXXXXX XXXXXXXXXXX XXXXXXXXXX******* @goldmansachs.com
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Job Details

About the Role

At Goldman Sachs, our Applied AI Engineer - Associate plays a pivotal role in advancing production machine-learning systems. You will work on cutting-edge AI/ML projects spanning generative AI, deep learning, natural language processing, and time series analysis in the financial domain.

Responsibilities

  • Collaborate with colleagues to advance production machine-learning systems.
  • Conceptualize, experiment with, and assess AI/ML-based software systems.
  • Develop, test, and maintain production-ready code.
  • Provide technical leadership on cross-team projects.
  • Create libraries and frameworks for reliable and testable systems.
  • Represent Goldman Sachs at conferences and in open-source communities.

Qualifications

A Master's or Ph.D. in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, Quantitative Finance or equivalent experience is required. Additionally, 1-3 years of industry experience in AI/ML and proficiency in programming languages like Python, C++, or Java are essential.

About Goldman Sachs

Founded in 1869, Goldman Sachs is a leading global investment banking, securities, and investment management firm headquartered in New York. The company provides competitive benefits and fosters diversity and inclusion across its global offices.

Key skills/competency

  • AI
  • Machine Learning
  • Deep Learning
  • NLP
  • Software Development
  • Quantitative Finance
  • Python
  • C++
  • Technical Leadership
  • Collaboration
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How to Get Hired at Goldman Sachs

🎯 Tips for Getting Hired

  • Customize your resume: Highlight AI and ML project experience.
  • Research Goldman Sachs: Understand the firm’s culture and values.
  • Showcase technical skills: Emphasize Python, C++, or Java expertise.
  • Network with employees: Leverage LinkedIn and industry events.

📝 Interview Preparation Advice

Technical Preparation

Review AI/ML algorithms and libraries.
Practice coding in Python, C++, and Java.
Study production-level software system design.
Work on real-world ML application cases.

Behavioral Questions

Describe your team collaboration experience.
Explain a challenging project you led.
Discuss how you handle feedback.
Share conflict resolution examples.

Frequently Asked Questions