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.
• 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.