Applied AI ML Engineer Lead

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Palo Alto, CA
Posted: 15 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $219k

$174k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $192,050–$246,150.

Based on 240 similar postings.

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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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Applied AI ML Engineer Lead

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As an Applied AI ML Engineer Lead within the Applied Artificial Intelligence and Machine Learning team in Commercial and Investment Banking, you will architect and deliver 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 and reference architectures. Your role involves translating business problems into research plans, establishing rigorous experimentation practices like ablation testing, and mentoring engineers. You will utilize modern programming languages, machine learning frameworks, and distributed computing patterns for training and serving. The work focuses on developing autonomous agent systems that reason, plan, act, and learn to solve high-impact problems in the financial services domain. Preferred skills include experience with AWS SageMaker or Bedrock and expertise in multi-agent coordination and tool-use.

Skills

Machine Learning Agentic AI Multi-agent Systems AWS SageMaker Amazon Bedrock Distributed Computing Software Engineering

What you'll do

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

What we're looking for

Advanced degree (Master's or Doctorate) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
Formal training or certification in applied artificial intelligence and machine learning concepts.
5+ years of experience in applied artificial intelligence and machine learning.
Proven track record of 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/serving and state management for agent workflows.
Demonstrated experience designing experiments and evaluations for machine learning systems with clear metrics.
Ability to lead through influence across cross-functional teams and translate complex technical topics.

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