Compliance-New York-Associate-Machine Learning Engineering

New York, New York, United States

Goldman Sachs

The Goldman Sachs Group, Inc. is a leading global investment banking, securities, and asset and wealth management firm that provides a wide range of financial services.

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YOUR IMPACT

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.
  • 2+ 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. 

Salary Range 
The expected base salary for this New York, New York, United States-based position is $115000-$180000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

Benefits 
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.

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Tags: Architecture Avro AWS Big Data Computer Science Deep Learning Distributed Systems Engineering Feature engineering GCP HDFS HuggingFace LLMs Machine Learning ML models Parquet Prompt engineering PySpark Python PyTorch Scala Scikit-learn TensorFlow Unstructured data UX

Perks/benefits: Career development Competitive pay Salary bonus

Region: North America
Country: United States

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