Applied AIML Lead Research Engineer

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 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 Champions the firm's culture of diversity, Opportunity, inclusion, and respect Required qualifications, capabilities, and skills 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 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