{"id":1206859,"url":"https://alion.io/job/jpmorganchase-applied-aiml-lead-research-engineer","title":"Applied AIML Lead Research Engineer","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":91,"open_postings":1209,"ghost_share":0.007,"stale_share":0.509,"repost_share":0.057,"time_to_fill_p50_days":10,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Glasgow, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":138000,"max_usd":267000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Tokenomics","optional":false},{"name":"Voice Agents","optional":false}],"status":"live","first_seen_at":"2026-07-28T16:59:50Z","employer_posted_date":"2026-07-28","last_verified_at":"2026-10-27T04:36:26Z","board_verified":true,"closed_at":null,"days_open":90,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":90},"description":"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.\nAs 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.\nThis is a Vice President-level role and an integral part of the IPB Tech AIML team, reporting to the Head of AI, IPB Tech.\nJob responsibilitiesLeads applied research into open-source LLM evaluation and deployment for cost, sovereignty, and data-residency optionality\nRuns fine-tuning work for tone, lexicon, and domain-specific tasks that are currently brittle in prompts\nInvestigates inference efficiency and tokenomics: cost-per-output, model routing, and optimisation of production inference\nPrototypes voice and image/multimodal capabilities (speech-to-text, text-to-speech, voice agents, document and image understanding) to underwrite future user-facing experiences\nDesigns rigorous evaluation methodologies and benchmarks so research findings are measurable and reproducible\nPartners with AI and platform engineers to productionise research outputs, each tied to a real platform or product need\nPublishes internal findings and raises the team's collective capability through knowledge-sharing; acts as a recruiting and thought-leadership magnet\nChampions the firm's culture of diversity, Opportunity, inclusion, and respect\nRequired qualifications, capabilities, and skillsFormal training or certification on artificial intelligence and machine learning concepts and applied experience\nAdvanced proficiency in Python and the modern ML/DL stack (e.g., PyTorch, Hugging Face)\nHands-on experience evaluating, fine-tuning, and deploying Large Language Models\nStrong grounding in experimental design, evaluation, and benchmarking of ML systems\nAbility to bridge research and engineering: prototypes that become production-ready capabilities\nAwareness of inference cost, 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