The Machine Learning Engineering role within Compliance Engineering at Goldman Sachs focuses on developing and deploying high-quality machine learning models to address complex regulatory challenges. The team consists of over 300 engineers and scientists dedicated to building platforms that mitigate regulatory risks. This position emphasizes working with large-scale structured and unstructured data while leveraging cutting-edge technologies. The firm is investing significantly in enhancing the precision and recall of its compliance models portfolio in 2024.

• A Bachelor's or Master's degree in Computer Science or a similar field of study.
• 10+ years of hands-on experience with building scalable machine learning systems.
• Solid coding skills and strong Computer Science fundamentals, including algorithms and data structures.
• Expertise in Python and PySpark.
• Experience with distributed technologies such as Scala, Pyspark, and AWS/GCP.

• Work with large-scale structured and unstructured data.
• Drive end-to-end machine learning projects that are complex and scalable.
• Build infrastructure for machine learning, including feature engineering and model scaling.
• Develop, productionize, and maintain machine learning models.
• Run ML experiments by tuning features and modeling approaches while documenting findings.

• Employees at Goldman Sachs are often offered comprehensive benefits, including medical, dental, life and disability coverage, generous vacation and holidays, and robust wellness resources such as EAP counseling, medical advocacy, on-site/virtual health services, and fitness support. Financial perks typically include retirement savings programs, live financial education, education support, and wealth-creation opportunities through equity awards and select investment programs. Many locations also provide family benefits (childcare resources, parental and family leaves, adoption/surrogacy support) and flexible work options like part-time schedules, job sharing, telecommuting, and alternate hours.