Senior Applied AI/ML Engineer, HR Data & Analytics

JPMorganChase; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorganChase is seeking a Sr. Associate – Applied AI/ML in HR Data and Analytics to advance AI-driven workforce solutions across the firm's HR function.

You will work with domain experts and cross‑functional teams to design and scale AI/ML systems from research to production deployment. The role emphasizes quantitative analysis, model evaluation, and collaboration across product managers, engineers, and stakeholders to deliver impact at scale, with opportunities for growth on a world‑class data

Qualifications

• Advanced degree in a quantitative field.
• 2+ years of AI/ML development and deployment experience.
• Strong Python programming with ML libraries.
• Expertise in ML techniques including NLP, generative AI, or CV.
• Practical experience with LLMs, fine-tuning, prompts, RAG pipelines.
• Understanding of agentic AI systems and design principles.
• Strong analytical and cross‑functional collaboration skills.

Responsibilities

• Engage with stakeholders to define business needs and design technical solutions.
• Collaborate across end‑to‑end AI/ML lifecycle from conception to production.
• Perform AI model evaluations and monitoring; ensure accuracy and reliability.
• Conduct research to advance AI/data science foundations.
• Work with teams across the firm to advance mission and employee experience.

Skills

Advanced degree

AI/ML experience

Python

ML techniques

NLP

LLMs

RAG pipelines

Agentic AI

Education

Master's or higher

PhD (preferred)

Tools

TensorFlow

PyTorch

Keras

scikit-learn

Job description

JPMorganChase is seeking a Sr. Associate – Applied AI/ML in HR Data and Analytics to advance AI-driven workforce solutions across the firm's HR function.

You will work with domain experts and cross‑functional teams to design and scale AI/ML systems from research to production deployment. The role emphasizes quantitative analysis, model evaluation, and collaboration across product managers, engineers, and stakeholders to deliver impact at scale, with opportunities for growth on a world‑class data