Applied Machine Learning Scientist - Vice President
On-site · Palo Alto, California, United States
Job Summary
Lead and deploy state-of-the-art ML systems across NLP, speech recognition, recommendation systems, and information retrieval; design and build agentic AI systems for multi-step workflows including tool/function calling, multi-agent orchestration, planning, grounding, and safety guardrails; apply reinforcement learning and PEFT techniques to optimize personalization, dialog policies, and production constraints; select and innovate ML strategies for banking problems; evaluate ongoing performance and ensure responsible AI usage and governance; collaborate with Business, Technology, Product, Design, Analytics, and Model Governance teams to deploy solutions; implement privacy, safety, and security controls for GenAI systems (PCI handling/redaction, policy checks, jailbreak resistance, auditability), and contribute to a knowledge-sharing culture.
Required Qualifications
- MS with 7+ years or PhD with 4+ years of hands-on industry experience in building and deploying ML systems (NLP/IR/Recommendation/GenAI) in production
- Strong understanding of NLP concepts, transformer architectures, knowledge distillation, transfer learning, and representation learning
- Applied GenAI experience with LLMs, prompt design, grounded generation, and RAG
- Experience scaling LLM systems (caching, batching, governance, evaluation harnesses)
- Proficiency in Python and ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn)
- Ability to design experiments, establish baselines, and evaluate model performance rigorously
- Solid understanding of Information Retrieval/recommendation concepts and/or related systems
- Strong communication and collaboration skills
- Ability to work independently and in cross-functional teams
Desired Qualifications
- Hands-on experience in building and deploying machine learning systems in production environments
- Strong deep learning and transformer-based modeling expertise
- Experience with LLMs and fine-tuning for targeted use cases
- Proficiency in Python and ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn)
- Experience with GenAI, RAG, tool/function-calling agents, and multi-agent systems
- Ability to design experiments, establish baselines, and evaluate model performance rigorously
- Experience in regulated finance domains and risk/control processes is a plus
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