Applied AI ML at JPMorgan Corporate Investment Bank combine cutting edge AI techniques with the company’s unique data assets to optimize business decisions and automate processes. 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.
We have a growing portfolio of AI–powered products and services and increasing opportunity for re-use of foundational components through careful design of libraries and services to be leveraged across the team.
Job responsibilities
- This role straddles the boundary between Scientific Research and Software Engineering and requires a deep understanding of both mindsets.
- Our Machine Learning Engineers collaborate closely with cloud and SRE teams but take a leading role in the design and delivery of the production architectures for our solutions.
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, deploying business critical machine learning models
- Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc
- Experience monitoring, maintaining, enhancing existing models over an extended time period
- Specialism in NLP or Computer Vision
- Solid understanding of fundamentals of statistics, optimization and ML theory
- Extensive experience with pytorch, numpy, pandas
- Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)
- Excellent grasp of comp sci fundamentals and dev best practice
- Able to understand business objectives and align ML problem definition
- Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.
Preferred qualifications, capabilities, and skills
- Experience designing/ implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
- Experience of big data technologies (e.g. Spark, Hadoop)
- Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.)
- Knowledge of open source datasets and benchmarks in NLP / Computer Vision
- Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
- Familiarity with GraphQL