JPMorgan Chase—Applied AI ML Senior Associate

Job description

Description: Join a world-class data science team at JPMorgan Chase and help shape the future of our Chief Administrative Office. As a Data Scientist Senior Associate in the Chief Data & Analytics Office, you will lead the development and deployment of innovative AI and machine learning solutions. You will collaborate with cross-functional teams to address complex business challenges, drive adoption of modern ML practices, and ensure responsible AI governance. You will have the opportunity to work with state-of-the-art technologies and contribute to a culture of technical excellence and continuous learning. Job responsibilities: - Lead the hands-on design, development, and deployment of advanced AI, GenAI, and large language model solutions. - Serve as a subject matter expert on a wide range of machine learning techniques and optimizations. - Collaborate with product, engineering, and business teams to deliver scalable, production-ready AI systems. - Conduct experiments using the latest ML technologies, analyze results, and tune models for optimal performance. - Own end-to-end code development in Python for both proof-of-concept and production-ready solutions. - Integrate generative AI within the ML platform using state-of-the-art techniques. - Drive adoption of modern ML infrastructure, tools, and best practices. - Optimize system accuracy and performance by identifying and resolving inefficiencies. - Communicate technical concepts and results to both technical and business stakeholders. - Ensure responsible AI practices, model governance, and compliance with regulatory standards. - Mentor and guide other AI engineers and scientists, fostering a culture of continuous learning. Required qualifications, capabilities, and skills: - Master’s or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field. - Minimum 5 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models. - Experience programming in Python; experience with ML frameworks such as PyTorch or TensorFlow. - Proven experience designing, training, and deploying large-scale ML/AI models in production environments. - Deep understanding of prompt engineering, agentic workflows, and orchestration frameworks. - Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray, Slurm). - Solid grasp of MLOps tools and practices (MLflow, model monitoring, CI/CD for ML). - Strong communication skills with the ability to explain complex technical concepts to diverse audiences. - Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners. - Experience applying data science and ML techniques to solve business problems and passion for detail, follow-through, and technical excellence. Preferred qualifications, capabilities, and skills: - Experience with high-performance computing and GPU infrastructure (e.g., NVIDIA DCGM, Triton Inference). - Familiarity with big data processing tools and cloud data services. - Advanced knowledge in reinforcement learning, meta learning, or related advanced ML areas. - Experience with search/ranking, recommender systems, or graph techniques. - Background in financial services or regulated industries. - Experience with building and deploying ML models on cloud platforms such as AWS Sagemaker, EKS, etc. - Published research or contributions to open-source GenAI/LLM projects.