Vice President, Machine Learning Engineer, Compliance

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 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 to improve the precision/recall of the Compliance models portfolio in 2024. To achieve that we are hiring experienced Machine Learning Engineers who have experience 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 structured 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.
• 6+ 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.

Key skills/competency

• Machine Learning
• Distributed Systems
• Python
• PySpark
• Deep Learning
• Feature Engineering
• Big Data
• System Design
• Cloud Platforms (AWS/GCP)
• Compliance