We're looking for a hands-on AI research engineer ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies.

As an Applied AI ML Lead- AI Research Engineer - Vice President at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you will lead applied research that positions the team for capabilities we will need over the coming years. You will evaluate and prototype emerging techniques (open-source LLMs, fine-tuning, efficient inference, and multimodal models) and bridge them into production-grade capabilities, working closely with the team's engineers so that research translates into shipped product rather than staying on the bench.

This is a Vice President-level role and an integral part of the IPB Tech AIML team, reporting to the Head of AI, IPB Tech.

Job Responsibilities

Leads applied research into open-source LLM evaluation and deployment for cost, sovereignty, and data-residency optionality
Runs fine-tuning work for tone, lexicon, and domain-specific tasks that are currently brittle in prompts
Investigates inference efficiency and tokenomics: cost-per-output, model routing, and optimisation of production inference
Prototypes voice and image/multimodal capabilities (speech-to-text, text-to-speech, voice agents, document and image understanding) to underwrite future user-facing experiences
Designs rigorous evaluation methodologies and benchmarks so research findings are measurable and reproducible
Partners with AI and platform engineers to productionise research outputs, each tied to a real platform or product need
Publishes internal findings and raises the team's collective capability through knowledge-sharing acts as a recruiting and thought-leadership magnet

Required qualifications, capabilities, and skills

Formal training or certification on artificial intelligence and machine learning concepts and 5+ years 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

Preferred qualifications, capabilities, and skills

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