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 rolePhD in a quantitative discipline, . Computer Science, Mathematics, StatisticsAble to understand business objectives and align ML problem definitionTrack record of solving real world problems with AIDeep specialism in NLP or Computer VisionDeep understanding of fundamentals of statistics, optimization and ML theoryExtensive experience with pytorch, numpy, pandasHands on experience finetuning modern deep learning architectures (transformers, CNN, autoencodersKnowledge of open source datasets and benchmarks in NLP or Computer VisionAble to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholdersExperience 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 purposesExperience 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