Join our team as a Sr. Associate Machine Learning Engineer to advance AI in financial services and optimize business decisions.

As a Senior Associate Machine Learning Engineer in the Applied AI ML team at JPMorgan Corporate Investment Bank, you'll be at the forefront of AI innovation, combining cutting-edge techniques with unique data assets to optimize business decisions and automate processes. This role offers a unique blend of scientific research and software engineering, allowing you to advance AI in financial services and build impactful products.

In this role, you will leverage the latest research in Natural Language Processing, Computer Vision, and statistical machine learning to build AI-powered products that automate processes and enhance decision-making. You will collaborate with software engineering teams to design scalable Machine Learning services and communicate AI capabilities to diverse audiences.

 

Job Responsibilities:

Build robust Data Science capabilities scalable across multiple business use cases. Collaborate with software engineering teams to design and deploy Machine Learning services. Research and analyze data sets using statistical and machine learning techniques. Communicate AI capabilities and results to both technical and non-technical audiences. Document approaches, techniques, and processes to comply with industry regulations. Collaborate with cloud and SRE teams in designing and delivering production architectures.

 

Required Qualifications, Capabilities, and Skills:

Hands-on experience in an ML engineering role. PhD in a quantitative discipline, e.g., Computer Science, Mathematics, Statistics. Track record of developing and deploying business-critical machine learning models. Broad knowledge of MLOps tooling for versioning, reproducibility, and observability. Experience monitoring and maintaining models over time. Specialism in NLP or Computer Vision. Solid understanding of statistics, optimization, and ML theory. Extensive experience with PyTorch, NumPy, and Pandas. Familiarity with deep learning architectures (e.g., transformers, CNN, autoencoders). Excellent grasp of computer science fundamentals and development best practices. Ability to communicate technical information clearly and build trust with stakeholders.

 

Preferred Qualifications, Capabilities, and Skills:

Experience designing/implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray). Experience with big data technologies (e.g., Spark, Hadoop). Hands-on experience with distributed/multi-threaded/scalable applications. Knowledge of open-source datasets and benchmarks in NLP/Computer Vision. Experience constructing batch and streaming microservices exposed as REST/gRPC endpoints. Familiarity with GraphQL.