Applied AI/ML at JPMorgan Corporate Investment Bank combine cutting edge AI techniques with the company’s vast and unique data assets to optimize business decisions and automate processes. We pride ourselves on being able to rapidly operationalize our solutions. In this role, you will be part of our industry-leading team, and advance the state-of-the-art in AI as applied to financial services. You will leverage the latest research from fields of Natural Language Processing, Computer Vision and statistical machine learning to build products that automate process, help experts prioritize their time and make better decisions.

Our scientists take the lead in translating business requirements into machine learning problems and ensure through ongoing literature review that our solutions leverage the most appropriate algorithms. 

Job responsibilities

This role is not a purely academic research role. As an applied team we are focused on rapidly delivering business value with our solutions.  Our scientists and engineers collaborate closely throughout the entire product lifecycle to ensure that experimental results are reproducible and we’re able to rapidly promote from “Proof of Concept” to production. The role is initially that of an individual contributor, though there will be optional opportunity for management responsibility dependent on the candidate’s experience.

Required qualifications, capabilities, and skills

Hands on experience in a commercial/ Postdoctoral Research role PhD in a quantitative discipline, . Computer Science, Mathematics, Statistics Able to understand business objectives and align ML problem definition Track record of solving real world problems with AI  Deep specialism in NLP or Computer Vision  Deep understanding of fundamentals of statistics, optimization and ML theory Extensive experience with pytorch, numpy, pandas Hands on experience finetuning modern deep learning architectures (transformers, CNN, autoencoders Knowledge of open source datasets and benchmarks in NLP or Computer Vision Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders Experience working collaboratively within a team to build software.

Preferred qualifications, capabilities, and skills

Experience pretraining foundation models (LLM / vision/ multimodal) Experience of documenting solutions for enterprise risk/ governance purposes Experience designing/ implementing pipelines using DAGs (. Kubeflow, DVC, Ray) Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, Experience of big data technologies (. Spark, Hadoop) Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc