Take a technical leadership position within JPMorgan's Commercial & Investment Bank, where you'll harness cutting-edge AI techniques to revolutionize business decisions and automate processes.
As an Applied AI / ML Lead – Vice President - Machine Learning Engineer in the Applied AI ML team at JPMorgan Commercial & Investment Bank, you will be at the forefront of 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. You will be instrumental in building products that automate processes, 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. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets. 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
Build robust Data Science capabilities which can be scaled across multiple business use cases Collaborate with software engineering team to design and deploy Machine Learning services that can be integrated with strategic systems Research and analyse data sets using a variety of statistical and machine learning techniques Communicate AI capabilities and results to both technical and non-technical audiences Document approaches taken, techniques used and processes followed to comply with industry regulation Collaborate closely with cloud and SRE teams while taking 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, . 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. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders Extensive experience with pytorch, numpy, pandas Knowledge of open source datasets and benchmarks in NLP / Computer Vision Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, 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 (. Kubeflow, DVC, Ray) Experience of big data technologies (. Spark, Hadoop) Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Familiarity with GraphQL
