Compliance-New York-Vice President-Software Engineering

Goldman Sachs•New York, NY

8h•Onsite

About The Position

The Compliance Engineering team is a global group of over 300 engineers and scientists focused on addressing complex, mission-critical challenges. They build and operate platforms and applications to prevent, detect, and mitigate regulatory and reputational risks. The team utilizes the latest technology and vast amounts of structured and unstructured data, leveraging modern frameworks for responsive UX/UI and Big Data applications. Within Compliance Engineering, this role is within Models Engineering, focusing on improving the precision and recall of Compliance models. The firm is investing significantly in this area in 2024 and is seeking experienced Machine Learning Engineers (MLEs) with a background in developing and deploying ML models for big data within a distributed architecture.

Requirements

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 (algorithms, data structures, software design).
Expertise in Python & PySpark.
Experience in working with distributed technologies like Scala, Pyspark, Iceberg, HDFS file formats (avro, parquet), AWS/ GCP, big data feature engineering.
Experience in system design and evaluating the pros and cons of database choices, schema definition for data storage.
Extensive experience with Machine Learning and Deep Learning toolkits (Tensorflow, PyTorch, Scikit-Learn, HuggingFace).

Nice To Haves

Prior experience with LLMs and Prompt Engineering.
Prior experience in architecting/ deploying ML applications on AWS/ GCP.
Prior experience in code reviews/ architecture design for distributed systems.

Responsibilities

Work with large scale structure and unstructured data.
Drive end to end Machine Learning projects that have a high degree of scale and complexity.
Build infra for machine learning which involves feature engineering and scaling models to work at scale.
Develop, productionize, and maintain ml models.
Run ML experiments by constantly tuning the features and the modeling approaches, documenting findings and results.
Collaborate closely with ML researchers, to accelerate the usage of cutting edge models.
Perform code reviews and ensure code quality.