Lead Machine Learning Engineer

JPMorgan Chase & Co.•New York, NY

12d•$171,000 - $260,000

About The Position

As Lead Machine Learning Engineer on the Digital Intelligence team within the Consumer & Community Banking division, you will collaborate with a high-caliber team of software developers and deep learning experts. You will build and maintain pipelines for distributed model training on large compute clusters, hyperparameter tuning at scale, model monitoring, and design and develop ML frameworks and components used for various model implementations. 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.

Requirements

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.

Nice To Haves

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).

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.