Machine Learning Engineer

Are you passionate about delivering mission-critical, high quality machine learning models, using cutting-edge technology, in a dynamic environment? 

OUR IMPACT

We are Compliance Engineering, a global team of more than 300 engineers and scientists who work on the most complex, mission-critical problems. 

We:

• build and operate a suite of platforms and applications that prevent, detect, and mitigate regulatory and reputational risk across the firm. 
• have access to the latest technology and to massive amounts of structured and unstructured data.
• leverage modern frameworks to build responsive and intuitive UX/UI and Big Data applications.

Within Compliance engineering, we are hiring for a Machine Learning Engineering role within Models Engineering. The firm is making a significant investment improve the precision/ recall of the Compliance models portfolio in 2024. To achieve that we are hiring experienced MLEs who have experience of developing and deploying ML models for big data in a distributed architecture.

HOW YOU WILL FULFILL YOUR POTENTIAL

As a member of our team, you will:

• 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

QUALIFICATIONS

A successful candidate will possess the following attributes:

• 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)

Experience in some of the following is desired and can set you apart from other candidates : 

• 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. 

ABOUT GOLDMAN SACHS

 

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 GS.com/careers. 

 

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

 

 

© The Goldman Sachs Group, Inc., 2023. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.