Job description

Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. In this role, you’ll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You’ll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows. As Lead Machine Learning Engineer on the Digital Intelligence team, you will be collaborating with a high-caliber team of software developers and deep learning experts, you’ll build and maintain pipelines for distributed model training on large compute clusters, hyperparameter tuning at scale, model monitoring, design and develop ML frameworks and components used for various model implementations. Job responsibilities • Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows. • Develop and manage pipelines for model promotion and other capabilities related to MDLC. • Optimize training throughput for large data sources • Establish and maintain integrations to platforms and tools related to model monitoring and observability • Collaborate with cross-functional teams to integrate new technologies and improve the capabilities of our ML Platform. • Partner with product, architecture, modeling, and engineering to design robust solutions that power our Digital channels   Required qualifications, capabilities, and skills • BS in Computer Science or related Engineering field with 6+ years of experience Or MS degree in Computer Science or related Engineering field with 4+ years experience. • Solid knowledge and extensive experience in Python and in cloud computing, along with ML frameworks (i.e. pytorch, tensorflow) • Deep knowledge and passion for data science fundamentals, training and deploying models • Experience in monitoring and observability tools to monitor model input/output and features stats • Operational experience in big data/ML tools such as Ray, Spark and in training/inference systems such as Ray, vllm/SGLang • Solid grounding in engineering fundamentals and enterprise system design Preferred qualifications, capabilities, and skills • Experience with recommendation and personalization systems is a plus. • CUDA experience is a big plus • Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc] • Good knowledge of data storage solutions and strategies (online and offline) Come join us in reshaping the future!

Requirements

Must-haves

Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
Develop and manage pipelines for model promotion and other capabilities related to MDLC.
Optimize training throughput for large data sources
Establish and maintain integrations to platforms and tools related to model monitoring and observability
Collaborate with cross-functional teams to integrate new technologies and improve the capabilities of our ML Platform.
Partner with product, architecture, modeling, and engineering to design robust solutions that power our Digital channels
Required qualifications, capabilities, and skills
BS in Computer Science or related Engineering field with 6+ years of experience Or MS degree in Computer Science or related Engineering field with 4+ years experience.
Solid knowledge and extensive experience in Python and in cloud computing, along with ML frameworks (i.e. pytorch, tensorflow)
Deep knowledge and passion for data science fundamentals, training and deploying models
Experience in monitoring and observability tools to monitor model input/output and features stats
Operational experience in big data/ML tools such as Ray, Spark and in training/inference systems such as Ray, vllm/SGLang
Solid grounding in engineering fundamentals and enterprise system design
CUDA experience is a big plus
Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc]
Good knowledge of data storage solutions and strategies (online and offline)
Come join us in reshaping the future!

Nice-to-haves

Preferred qualifications, capabilities, and skills
Experience with recommendation and personalization systems is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmmachine learningmlpythondockerkubernetes

Platform: oracle-cloud
Effort: medium

Benefits

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Vision