Applied Aiml Lead Research Engineer

JPMorgan Chase JPMorgan Chase · Banking · GLASGOW, LANARKSHIRE, United Kingdom · Asset & Wealth Management

Lead applied research for an AI/ML team within a financial institution, focusing on evaluating and prototyping emerging techniques like open-source LLMs, fine-tuning, efficient inference, and multimodal models. The role involves bridging research into production-grade capabilities, working closely with engineers, and designing evaluation methodologies. This is a Vice President-level role.

What you'd actually do

  1. Leads applied research into open-source LLM evaluation and deployment for cost, sovereignty, and data-residency optionality
  2. Runs fine-tuning work for tone, lexicon, and domain-specific tasks that are currently brittle in prompts
  3. Investigates inference efficiency and tokenomics: cost-per-output, model routing, and optimisation of production inference
  4. Prototypes voice and image/multimodal capabilities (speech-to-text, text-to-speech, voice agents, document and image understanding) to underwrite future user-facing experiences
  5. Designs rigorous evaluation methodologies and benchmarks so research findings are measurable and reproducible

Skills

Required

  • Formal training or certification on artificial intelligence and machine learning concepts and applied experience
  • Advanced proficiency in Python and the modern ML/DL stack (e.g., PyTorch, Hugging Face)
  • Hands-on experience evaluating, fine-tuning, and deploying Large Language Models
  • Strong grounding in experimental design, evaluation, and benchmarking of ML systems
  • Ability to bridge research and engineering: prototypes that become production-ready capabilities
  • Awareness of inference cost, performance, and optimisation techniques
  • Strong communication skills, including translating research into business and engineering terms
  • Published in a top-tier AIML venue (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR), or equivalent open-source / applied-research contributions in LLMs, fine-tuning, or efficient inference
  • MSc or PhD in Machine Learning, Computer Science, or a related quantitative field, or equivalent applied experience

Nice to have

  • Experience with speech / voice models and audio ML
  • Experience within financial services, particularly wealth, private banking, or asset management
  • Familiarity with data-residency, sovereignty, and Responsible AI considerations for regulated environments
  • Familiarity with JPM-internal AI/ML infrastructure for internal candidates

What the JD emphasized

  • Published in a top-tier AIML venue (e.g. NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR), or equivalent open-source / applied-research contributions in LLMs, fine-tuning, or efficient inference

Other signals

  • applied research
  • prototype emerging techniques
  • bridge them into production-grade capabilities
  • evaluate and prototype emerging techniques
  • fine-tuning
  • efficient inference
  • multimodal models
  • production-grade capabilities
  • shipped product