Location
London

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
Join a high performing team of applied AI experts to drive innovation and new capabilities in the Commercial & Investment Bank. As an Applied AI / ML Senior Associate 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 such as 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. Our growing portfolio of AI-powered products and services offers increasing opportunities for re-use of foundational components through careful design of libraries and services leveraged across the team. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets.

Job Responsibilities
- Build robust Data Science capabilities scalable across multiple business use cases
- Collaborate with software engineering teams to design and deploy Machine Learning services 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, techniques, and processes to comply with industry regulations
- Collaborate closely with cloud and SRE teams, leading design and delivery of production architectures
- Act as an individual contributor with optional management responsibilities depending on experience

Experience
- Extensive experience with PyTorch and related Python data science libraries (e.g. pandas)
- Experience containerising applications or models for deployment (Docker)
- Experience with one of the major public cloud providers (Azure, AWS, GCP)
- Experience monitoring, maintaining, and enhancing existing models over extended periods
- Ability to communicate technical information clearly and build trust with stakeholders at all levels

About You
- Strong collaborative skills to work effectively with cross-functional teams including software engineering, cloud, and SRE
- Passionate about advancing AI in financial services with a blend of scientific research and software engineering mindset
- Proactive and able to lead design and delivery of production-ready ML solutions

Qualifications
- Masters or PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics
- Solid understanding of statistics, optimization, and machine learning theory
- Familiarity with popular deep learning architectures (transformers, CNNs, autoencoders)
- Specialism or well-researched interest in Natural Language Processing (NLP)
- Broad knowledge of MLOps tooling for versioning, reproducibility, and observability

Preferred Qualifications
- Experience designing and implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
- Experience with big data technologies
- Experience constructing batch and streaming microservices exposed as REST/gRPC endpoints
- Experience with container orchestration tools (e.g. Kubernetes, Helm)
- Knowledge of open source datasets and benchmarks in NLP
- Hands-on experience implementing distributed, multi-threaded, scalable applications
- Proven track record of developing and deploying business-critical machine learning models