As an Applied AI ML - Sr. Associate - Machine Learning Engineer within JPMorgan Corporate Investment Bank, you will be part of our industry-leading team, advancing 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. This role straddles the boundary between Scientific Research and Software Engineering and requires a deep understanding of both mindsets.

Job responsibilities

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• 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

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• 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, including nowledge of open source datasets and benchmarks
  
• Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)
  
• Extensive experience with pytorch, numpy, pandas
  
• Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.)
  
• Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.
  

Preferred qualifications, capabilities, and skills

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• Experience designing/ implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
  
• Experience of big data technologies (e.g. Spark, Hadoop)
  
• Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
  
• Familiarity with GraphQL
  

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