Applied Machine Learning Scientist Vice President
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Job Location:
Palo Alto, CA - USA
Monthly Salary:
$ 164350 - 260000
Posted on:
11 hours ago
Vacancies:
1 Vacancy
Job Summary
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.
As an Applied Machine Learning Scientist within our dynamic team 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.
Job Responsibilities
Lead and deploy state-of-the-art advanced machine learning systems across NLP speech recognition recommendation systems and information retrieval.
Design and build agentic AI systems for multistep workflows including tool/function calling multiagent orchestration planning grounding and safety guardrails.
Use reinforcement learning (policy optimization bandits RLHFstyle approaches where appropriate) to improve personalization dialog policies and sequential decisionmaking systems.
Fine-tune and adapt LLMs/SLMs using PEFT (LoRA AdaLoRA IA3) distillation and quantization; optimize for quality latency cost and production constraints.
Select and innovate on ML strategies for various banking problems.
Analyze and evaluate the ongoing performance of developed ML systems.
Collaborate with multiple partner teams such as Business Technology Product Management Design Analytics and Model Governance to deploy solutions into production.
Build domain understanding to identify high-impact opportunities ensure responsible AI usage and drive measurable outcomes (customer experience automation accuracy and efficiency).
Implement privacy safety and security controls for GenAI systems including PCI handling/redaction policy checks jailbreak resistance and auditability.
Required qualifications capabilities and skills
MS 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
Good understanding of the latest advancement of NLP concepts such as the transformer architecture knowledge distillation transfer learning and representation learning.
Applied GenAI experience with LLMs and the ability to finetune and deploy SLMs for targeted use cases familiarity with prompt design grounded generation and RAG.
Experience with scaling LLM systems (caching batching prompt/version governance evaluation harnesses)
Strong foundation in machine learning deep learning and statistical modelling including model evaluation and error analysis.
Solid understanding of Information Retrieval concepts (indexing ranking dense/sparse retrieval re-ranking) and/or recommendation systems.
Ability to design experiments establish strong baselines choose meaningful metrics and evaluate model performance rigorously
Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
Proficiency in Python and common ML libraries (PyTorch/TensorFlow Hugging Face scikit-learn) and ability to write production-quality code.
Ability to collaborate in cross-functional environments with product engineering and control partners.
Solid written and spoken communication skills
Preferred qualifications capabilities and skills
5 years of hands-on experience with virtual assistant model development and optimization
Experience orchestrating multiagent teams with supervisor agents debate/consensus mechanisms and rolespecialized toolkits for complex enterprise tasks.
Building agent governance and eval suites: redteaming adversarial tests safety scorecards regression suites for prompts/tools
Experience with RL/bandits preference optimization or human feedback loops for personalization.
Experience in regulated finance domains and working with risk/control processes.
Experience with MLOps/LLMOps: CI/CD for models monitoring/alerting model versioning evaluation of pipelines and rollback strategies.
Experience with A/B experimentation and data/metric-driven product development.
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such an employment offer for this position is contingent on JPMorganChases review of criminal conviction history including pretrial diversions or program entries.
Required Experience:
Exec
About Company
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans ov ... View more
