Applied AI ML Engineer Lead

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Palo Alto, CAJersey City, NJ
Employment
Full-time
Posted
61 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $223k
$163k $290k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $191,537–$254,750.

Based on 240 similar postings.

Employer

About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Applied AI ML Engineer Lead

The Applied AI ML Engineer Lead joins the Applied Artificial Intelligence and Machine Learning team within Commercial and Investment Banking. This role involves architecting and delivering generative artificial intelligence and agent-based solutions to automate complex operational workflows. You will build multi-agent systems that collaborate, coordinate tasks, and improve through feedback while designing reusable services, libraries, and reference architectures. Key responsibilities include establishing rigorous experimentation practices, including ablation testing and error analysis, and mentoring engineers. Required skills include an advanced degree in a quantitative field, five years of experience, and proficiency in modern programming languages and machine learning frameworks. You must demonstrate experience with distributed computing patterns, state management for agent workflows, and deploying production machine learning applications. The work focuses on solving high-impact business problems through autonomous agentic AI.

What you'll do

  • Architect and deliver generative AI and agent-based solutions to automate complex operational workflows.
  • Translate business problems into research plans, success metrics, and scalable solution designs.
  • Build multi-agent systems that coordinate tasks and improve through feedback and evaluation.
  • Design reusable services, libraries, and reference architectures to accelerate AI adoption.
  • Establish rigorous experimentation practices including offline/online evaluation and error analysis.
  • Mentor and develop applied AI engineers and researchers to foster scientific rigor.
  • Partner with stakeholders to scale AI solutions while balancing performance, reliability, and security.

What we're looking for

  • Formal training or certification in applied artificial intelligence and machine learning concepts.
  • 5+ years of applied experience in artificial intelligence and machine learning.
  • Advanced degree (Master’s or Doctorate) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
  • Demonstrated experience designing experiments and evaluations for machine learning systems with clear metrics and reproducible results.
  • Proven track record deploying machine learning applications into production environments with operational excellence.
  • Strong software engineering skills in modern programming languages and machine learning frameworks.
  • Experience with distributed computing patterns for training and serving and state management for agent workflows.
  • Experience building and evaluating autonomous agent systems (preferred).

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