Senior Applied AI ML Engineer

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Jersey City, NJ
Posted
9 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $214k
$162k most similar roles pay here $274k

This listing doesn't post a salary. Most similar roles pay $172,900–$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 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 · Senior Applied AI ML Engineer

As a Senior Applied AI ML Engineer, you will join the team to design, build, and productionize risk typologies, data pipelines, supervised and unsupervised machine learning models, and LLM risk explainability. You will work at the intersection of risk modeling and NLP architectures to develop solutions for detecting AML risk, regulatory violations, transaction risks, and behavioral anomalies within high-volume banking transactions. Your daily responsibilities include building feedback measurement techniques, managing Databricks pipelines, and monitoring for drift or bias. The role requires expertise in Python, Java, SQL, and NoSQL, alongside experience with PyTorch, TensorFlow, SciKit, NeMo, and Huggingface Transformers. You will integrate models into cloud-scale microservices using AWS and Databricks while navigating complex domains like financial crime monitoring and inference optimization to ensure auditability and reliability for enterprise-grade applications.

What you'll do

  • Design, build, and operate supervised and unsupervised machine learning models for risk detection.
  • Develop and productionize LLM solutions focused on risk explainability and auditability.
  • Build and manage data pipelines for risk features within the Databricks environment.
  • Create feedback mechanisms and techniques to measure the accuracy of AI solutions.
  • Monitor systems to detect and alert on model drift, bias, and performance degradation.
  • Integrate machine learning models into cloud-scale, microservices-based architectures.
  • Develop production-grade ML pipelines, APIs, and MLOps frameworks for high-volume transactions.

What we're looking for

  • PhD in Computer Science or related field with 2+ years of experience.
  • MS in Computer Science or related field with 4+ years of experience.
  • BS in Computer Science or related field with 8+ years of experience.
  • Strong foundation in Information Retrieval and Natural Language Processing.
  • Expertise in Python and Java for functional programming and JVM-based systems.
  • Experience integrating models into cloud-scale, microservices-based architectures using AWS and Databricks.
  • Proficiency with ML frameworks such as PyTorch, TensorFlow, SciKit, NeMo, or Huggingface Transformers.
  • Operational experience supporting enterprise-grade ML applications in production environments.

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