Senior Lead Software Engineer - Machine Learning Engineer

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 deployments
• Extends machine learning libraries and frameworks to support complex requirements
• Designs thoughtful data scientist experience in delivering AI experience APIs and SDKs for the platform
• Collaborates 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 experience
• Advanced 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 evaluation
• Expert 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 Terraform
• Knowledge of Big Data and related technologies such as Hadoop, Spark, and Airflow

• Background in high performance computing and ML hardware acceleration
• Track record of contributing to open-source ML frameworks
• Knowledge of Kubernetes ecosystem, including EKS, Helm, and Custom Operators