{"id":2897382,"url":"https://alion.io/job/jpmorganchase-machine-learning-scientist-vice-president","title":"Machine Learning Scientist - Vice President","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":89,"open_postings":1100,"ghost_share":0.022,"stale_share":0.571,"repost_share":0.055,"time_to_fill_p50_days":14,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Plano, United States","Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":211000,"max_usd":440000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":92},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"PEFT","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Speech Recognition","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transfer Learning","optional":false},{"name":"CI/CD","optional":true},{"name":"LLMOps","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-01-28T22:15:02Z","employer_posted_date":"2026-01-28","last_verified_at":"2026-11-06T14:23:17Z","board_verified":true,"closed_at":null,"days_open":281,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":281},"description":"We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company.\nAs an Applied AI Machine Learning Lead, you will lead the development of scalable, production-grade advanced ML solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will play a key role in delivering Generative AI capabilities - designing and productionizing LLM-powered systems such as RAG (Retrieval Augmented Generation), tool/function-calling agents, and structured generation to automate complex workflows and improve customer experiences. You will collaborate with product, engineering, and control partners to translate ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. You bring strong deep learning and transformer-based modeling expertise, as well as hands-on experience in fine-tuning and evaluation. You must have a strong passion for machine learning, strong analytical thinking, a deep desire to learn, and high motivation. You must also invest independent time in learning, researching, and experimenting with new innovations, and contribute to a strong knowledge-sharing culture.\nJob responsibilities\nLead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.\n\nDesign and build agentic AI systems for multi-step workflows, including tool/function calling, multi-agent orchestration, planning, grounding, and safety guardrails.\n\nUse reinforcement learning (policy optimization, bandits, RLHF-style approaches where appropriate) to improve personalization, dialog policies, and sequential decision-making systems.\n\nFine-tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.\n\nSelect and innovate on ML strategies for various banking problems.\n\nAnalyze and evaluate the ongoing performance of developed ML systems.\n\nCollaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.\n\nBuild domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).\n\nImplement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.\n\nRequired qualifications, capabilities, and skills\nMS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment\n\nGood understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.\n\nApplied GenAI experience with LLMs and the ability to fine-tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.\n\nExperience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)\n\nStrong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.\n\nSolid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re-ranking) and/or recommendation systems.\n\nAbility to design experiments - establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously\n\nScientific thinking with the ability to invent and to work both independently and in highly collaborative team environments\n\nProficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn), and ability to write production-quality code.\n\nAbility to collaborate in cross-functional environments with product, engineering, and control partners.\n\nSolid written and spoken communication skills\n\nPreferred qualifications, capabilities, and skills\n5 years of hands-on experience with virtual assistant model development and optimization\n\nExperience orchestrating multi-agent teams with supervisor agents, debate/consensus mechanisms, and role-specialized toolkits for complex enterprise tasks.\n\nBuilding agent governance and eval suites: red-teaming, adversarial tests, safety scorecards, regression suites for prompts/tools\n\nExperience with RL/bandits, preference optimization, or human feedback loops for personalization.\n\nExperience in regulated finance domains and working with risk/control processes.\n\nExperience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.\n\nExperience with A/B experimentation and data/metric-driven product development.","description_format":"text","description_chars":5070,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial & Retail Banks","Wealth Management & Financial Advisors","Asset Management & Funds","Investment Banking & M&A Advisory"],"lifecycle":[{"event":"open","at":"2026-10-20T05:20:01Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 3,429 · green card filings: 933","filings_12m":3429,"filings_prev_12m":3601,"green_card_filings_12m":933,"median_offered_wage_usd":160366,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":2086}],"liveness":{"score":7,"band":"cold","label":"Long shot","p_open":1,"p_active":0.267,"p_room":0.28,"age_days":281,"expected_fill_days":14,"reasons":["conf:2","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-11-06T05:58:05Z"},"pay":null,"html_url":"https://alion.io/job/jpmorganchase-machine-learning-scientist-vice-president","json_url":"https://alion.io/job/jpmorganchase-machine-learning-scientist-vice-president.json","meta":{"generated_at":"2026-11-06T15:47:26Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":29473,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-07T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":257},"rest":"https://alion.io/mcp/rest/get_company?id=257"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fjpmorganchase-machine-learning-scientist-vice-president"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fjpmorganchase-machine-learning-scientist-vice-president"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fjpmorganchase-machine-learning-scientist-vice-president"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/jpmorganchase-machine-learning-scientist-vice-president\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fjpmorganchase-machine-learning-scientist-vice-president"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":2897382}},{"tool":"search_jobs","arguments":{"company":"jpmorganchase.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":2897382},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}