As an Applied AI ML Lead in the JPMorgan Corporate Investment Bank, you will be part of our industry-leading team, combining cutting-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state-of-the-art in AI as applied to financial services, leveraging the latest research from fields of Natural Language Processing, Computer Vision, and statistical machine learning to build products that automate processes, 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.
The role is initially that of an individual contributor, though there will be optional opportunity for management responsibility dependent on the candidate’s experience.
Job responsibilities
Focus on rapidly delivering business value with our Applied AI ML solutions. Collaborate closely with ML engineers 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
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.
