This role pays more than
60%
of similar roles. Most pay
$177,600–$254,750
— the shaded band above.
At the midpoint, this role pays about
$238k
versus about
$216k
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
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As an Applied AI ML professional, you will join the team to design and develop automation-based systems for data analysis, document management, and client intelligence. You will research machine learning methods for data processing and build large-scale frameworks to accelerate model application across various business areas. Your daily work involves developing models for Natural Language Processing, speech recognition, time-series predictions, reinforcement learning, and recommendation systems. You will utilize a technical stack including PyTorch, TensorFlow, HuggingFace, Keras, Scikit-learn, NLTK, spaCy, Rasa NLU, NumPy, Pandas, and Spark. Additionally, you will deploy models on AWS, Azure, or GCP using Docker and CI/CD pipelines like Jenkins or GitHub Actions. You will apply techniques such as RNNs, CNNs, and Transformer encoders like BERT to solve complex problems while performing A/B testing and tracking metrics via TensorBoard.
Design and develop automation-based systems for data analysis, document management, and client intelligence.
Research and develop machine learning models for NLP, speech recognition, time-series prediction, and recommendation systems.
Build large-scale frameworks to accelerate the application of machine learning across different business areas.
Develop and deploy production-ready ML experiments including data ingestion, feature extraction, training, and fine-tuning.
Evaluate model performance using intrinsic/extrinsic metrics aligned with business goals and cost-saving KPIs.
Process large datasets and train scalable models on multi-CPU/GPU environments using cloud-native services.
Implement CI/CD pipelines for ML models using tools like Jenkins, GitLab, or GitHub Actions.
Containerize and deploy machine learning artifacts using Docker.
What we're looking for
Master's degree in Computational Data Science, Computer Science, Electrical Engineering, Mathematics, Operations Research, Data Science, or a related field.
At least 1 year of experience as an Applied AI ML, Software Engineer, Analyst in Engineering Division, R&D Team, or related occupation.
Experience developing and deploying production-ready NLP and speech recognition systems.
Experience designing machine learning experiments and frameworks including data ingestion, training, fine-tuning, evaluation, and monitoring.
Proficiency with distributed deep learning and data processing frameworks such as PyTorch, TensorFlow, HuggingFace, Keras, SKlearn, NLTK, spaCy, Rasa NLU, Numpy, Pandas, and Spark.
Experience deploying models on multiple CPUs/GPUs using cloud-native managed services like AWS, Azure, or GCP.
Proficiency in machine learning techniques including logistic regression, gradient-boosted trees, RNNs, and CNNs for NLP and speech applications.
Experience with Elasticsearch, Transformer encoders (BERT, Sentence-Transformers), and CI/CD tools like Jenkins, GitLab CI/CD, or GitHub Actions.