Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

Are you ready to make a significant impact in the world of AI and machine learning? At JPMorgan Chase, In this role, you'll be a key player in an agile team dedicated to enhancing, building, and delivering market-leading technology products that are secure, stable, and scalable

As a Senior Lead Software/Machine Learning Engineer, at JPMorgan Chase within the Corporate Sector – AIML Data Platforms Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

Architects and implements distributed ML experimentation and training platform for firm-wide use.Designs, implements, and supports tools and workflows to facilitate machine learning experiments, automated training runs, and production deploymentsExtends machine learning libraries and frameworks to support complex requirementsDesigns thoughtful data scientist experience in delivering AI experience APIs and SDKs for the platformCollaborates with infrastructure engineering, product management, and security and compliance teams to deliver tailored, robust solutions

 

Required qualifications, capabilities, and skills

Formal training or certification on software engineering  concepts and 5+ years applied experienceAdvanced knowledge of architecture, design, and software development processes.Deep understanding and hands-on experience with public cloud technologies, especially with AWS, in the context of ML engineering workflows, specifically featurization, experimentation, training, and evaluationExpert programming skills in at least Python and experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, JAX, etc.Hands-on experience implementing DevOps practices using tools such as Docker, Jenkins, Spinnaker, and TerraformKnowledge of Big Data and related technologies such as Hadoop, Spark, and Airflow

 

Preferred qualifications, capabilities, and skillsBackground in high performance computing and ML hardware accelerationTrack record of contributing to open-source ML frameworksKnowledge of Kubernetes ecosystem, including EKS, Helm, and Custom Operators