Compliance, Machine Learning Engineer, Dallas, Vice President

Goldman Sachs; Dallas (TX); On-site; USD 130,000 - 160,000; Full time

Job summary

Goldman Sachs in Dallas, Texas is looking for an experienced professional to build and maintain scalable machine learning systems. The role requires collaboration with researchers, expertise in Python and PySpark, and at least 10 years of hands-on experience in machine learning. Candidates should be familiar with AWS or GCP and various machine learning frameworks. You will have the opportunity to work with significant data and cutting-edge technology in a dynamic environment.

Qualifications

• 10+ years of hands-on experience building scalable machine learning systems.
• Strong coding skills and solid fundamentals in algorithms, data structures, and software design.
• Extensive experience with machine learning and deep learning toolkits.

Responsibilities

• Build infrastructure for machine learning that involves feature engineering and scaling models.
• Collaborate closely with ML researchers to accelerate the usage of cutting-edge models.
• Perform code reviews and ensure code quality.

Skills

Python

PySpark

Machine Learning

Deep Learning

Big Data

Education

Bachelor’s or Master’s degree in Computer Science or a similar field

Tools

TensorFlow

PyTorch

Scikit-Learn

HDFS

AWS

GCP

Job description

Job Description

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.

• 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 massive amounts of structured and unstructured data.
• Leverage modern frameworks to build responsive and intuitive UX/UI and Big Data applications.

How You Will Fulfill Your Potential

• Work with large scale structured and unstructured data, driving end‑to‑end machine learning projects that have a high degree of scale and complexity.
• Build infrastructure for machine learning that involves feature engineering and scaling models to work at scale.
• Develop, productionize, and maintain ML models.
• Run ML experiments by constantly tuning features and 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

• Bachelor’s or Master’s degree in Computer Science or a similar field.
• 10+ years of hands‑on experience building scalable machine learning systems.
• Strong coding skills and solid fundamentals in algorithms, data structures, and software design.
• Expertise in Python and PySpark.
• Experience with distributed technologies such as Scala, PySpark, Iceberg, HDFS file formats (AVRO, PARQUET), AWS/GCP, and big data feature engineering.
• Experience in system design and evaluating database choices and schema definition for data storage.
• Extensive experience with machine learning and deep learning toolkits (TensorFlow, PyTorch, Scikit‑Learn, HuggingFace).

Desired Experience

• Prior experience with large language models and prompt engineering.
• Prior experience architecting or deploying ML applications on AWS or GCP.
• Prior experience in code reviews and architecture design for distributed systems.

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