Applied AI ML Senior Associate

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.