Applied AI ML Lead

JPMorgan Chase

Quick summary

Work type
On-site
Location
Palo Alto, CA
Salary
$185,900–$260,000 / yr
Posted
10 days ago
Closes
Jul 8, 2026
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $212k
This role $223k
$162k most similar roles pay here $271k

This role pays more than 65% of similar roles. Most pay $177,012–$246,150 — the shaded band above. At the midpoint, this role pays about $223k versus about $212k for comparable roles.

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 394 open roles on FindRole.

Listed pay typically runs $152,000–$215,000 across 207 roles with salary data.

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View all roles at JPMorgan Chase

At a glance

TL;DR · Applied AI ML Lead

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As a Senior Machine Learning Engineer on the financial services team, you will design and deploy prompt-based models on large language models (LLMs) to address various natural language processing tasks. Your day-to-day responsibilities include conducting research on advanced prompt engineering techniques, collaborating with cross-functional teams to identify requirements, and developing scalable data pipelines using cloud services like PySpark and SQL. You will also build tools for model training, evaluation, and optimization, focusing on enhancing OCR models and integrating structured data into financial document processing workflows. Essential skills include expertise in Python, SQL, TensorFlow or PyTorch, Apache Spark, Jupyter Lab, and version control systems such as GitHub. This role requires a Master's degree plus 3 years of relevant experience, with a focus on applying machine learning techniques to improve accuracy and efficiency in financial data reconciliation and entity validation processes.

Skills

Python SQL PySpark Apache Spark Scikit-Learn TensorFlow PyTorch Keras Jupyter Lab Tableau Shapley Analysis Shell scripting Bash scripting GitHub Bitbucket NLP TF-IDF ARIMA SARIMA Regression techniques Classification methods Ensemble Methods Clustering Dimensionality Reduction Logistic Regression Decision Tree Random Forest Support Vector Machine Naive Bayes K-Nearest Neighbors Neural Network RNN LSTM CNN

What you'll do

  • Design and deploy prompt-based models on LLMs for financial NLP tasks.
  • Conduct research to improve performance of prompt-based models using advanced techniques.
  • Build data pipelines and workflows for scalable prompt engineering with cloud services.
  • Develop tools and frameworks for training, evaluating, and optimizing prompt-based models.
  • Lead model evaluation efforts applying both supervised and unsupervised techniques.
  • Implement NLP-powered reconciliation frameworks to streamline financial break resolution.

What we're looking for

  • Master's degree in Computer Engineering, Electrical Engineering, Computer Science, Data Science or related field plus 3 years of relevant experience
  • Expertise in SQL and Python for data manipulation, structuring, design flow, and query optimization
  • Proficiency in PySpark and SQL for large dataset processing and exploratory data analysis
  • Experience with advanced machine learning techniques including NLP models like TF-IDF and embeddings
  • Ability to develop neural network models using Keras, TensorFlow, or PyTorch
  • Skilled in Python programming utilizing Scikit-Learn, PyTorch, or TensorFlow frameworks
  • Knowledge of big data technologies such as Apache Spark and tools like Jupyter Lab and Tableau

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