Applied AI ML Lead - Machine Learning Engineer, Greater London

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Applied AI ML Lead – Machine Learning Engineer, Greater London

Client:

Location: Greater London, United Kingdom

Job Category:

–

EU work permit required:

Yes

Job Reference:

332109fae6ca

Job Views:

7

Posted:

03.03.2025

Expiry Date:

17.04.2025

Job Description:

As an Applied AI ML Lead in the JPMorgan Corporate Investment Bank, you will be part of our industry-leading team, 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 to build products that automate processes, help experts prioritize their time, and make better decisions.

Our scientists take the lead in translating business requirements into machine learning problems and ensure through ongoing literature review that our solutions leverage the most appropriate algorithms.

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

• Focus on rapidly delivering business value with our Applied AI ML solutions.
• Collaborate closely with ML engineers throughout the entire product lifecycle to ensure that experimental results are reproducible and we’re able to rapidly promote from “Proof of Concept” to production.

Required qualifications, capabilities, and skills

• Hands-on experience in a commercial/Postdoctoral Research role.
• Able to understand business objectives and align ML problem definition.
• Track record of solving real-world problems with AI.
• Deep specialism in NLP or Computer Vision.
• Deep understanding of fundamentals of statistics, optimization, and ML theory.
• Extensive experience with PyTorch, NumPy, Pandas.
• Hands-on experience fine-tuning modern deep learning architectures (transformers, CNN, autoencoders).
• Knowledge of open source datasets and benchmarks in NLP or Computer Vision.
• Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.
• Experience working collaboratively within a team to build software.

Preferred qualifications, capabilities, and skills

• Experience pretraining foundation models (LLM/vision/multimodal).
• Experience of documenting solutions for enterprise risk/governance purposes.
• Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed).
• Experience of big data technologies (e.g., Spark, Hadoop).
• Broad knowledge of MLOps tooling – for versioning, reproducibility, observability, etc.

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