Join our team as an instrumental applied ML engineer in building products that automate processes, helping experts to prioritize their time, and make better decisions. We have a growing portfolio of AI-powered products and services and increasing opportunities 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.
As a Gen AI Lead Engineer in the Applied AI/ML team at JPMorgan Corporate 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 the fields of natural language processing, computer vision, and statistical machine learning.
Job responsibilities
* Build robust data science capabilities which can be scaled across multiple business use cases.
* Collaborate with the software engineering team to design and deploy machine learning services that can be integrated with strategic systems.
* Research and analyze 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 regulations.
* 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
* Extensive hands-on experience in an ML engineering role.
* Extensive experience developing AI-based applications.
* PhD in a quantitative discipline, e.g., Computer Science, Mathematics, Statistics.
* Track record of developing and deploying business-critical machine learning models.
* Broad knowledge of MLOps tooling—for versioning, reproducibility, observability, etc.
* Experience monitoring, maintaining, and enhancing existing models over an extended time period.
* Specialism in NLP or computer vision.
* Solid understanding of the fundamentals of statistics, optimization, and ML theory.
* Extensive experience with PyTorch, NumPy, and pandas.
* Familiarity with popular deep learning architectures (e.g., transformers, CNNs, autoencoders).
* 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 with big data technologies (e.g., Spark, Hadoop).
* Hands-on experience in implementing distributed/multi-threaded/scalable applications (including frameworks such as Ray, Horovod, DeepSpeed, etc.).
* Knowledge of open-source datasets and benchmarks in NLP/computer vision.
* Experience constructing batch and streaming microservices exposed as REST/gRPC endpoints.
* Familiarity with GraphQL.
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