Applied AI ML Lead

JPMorganChase•Jersey City, NJ

About The Position

As an Applied AI and Machine Learning Lead at JPMorganChase within the AI and Machine Learning and Data Platform team in Corporate Sector, you will drive the design and delivery of machine learning and deep learning solutions that address business problems. You will own work from problem framing and experimentation through productionization, ensuring solutions are robust, scalable, and measurable. You will also contribute to technical excellence through mentorship, strong engineering practices, and continuous learning. The role involves applying a broad range of machine learning techniques, including large language models, and collaborating with product and engineering teams to develop science- and technology-driven solutions. You will conduct experiments, analyze results, tune models, and turn findings into production-ready solutions. You will also integrate Generative AI into the ML Platform and influence product design, application functionality, and technical operations and processes.

Requirements

MS and/or PhD in Computer Science, Machine Learning, or a related field.
At least 5 years of applied machine learning experience.
At least 5 years of experience with a programming language such as Python, Java, or C/C++; intermediate Python proficiency is required.
At least 5 years of experience applying data science and ML techniques to solve business problems.
Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs).
Experience with machine learning and deep learning methods.
Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow.
Experience in advanced applied ML areas such as GPU optimization, fine-tuning, embedding models, inferencing, prompt engineering, evaluation, and RAG (Similarity Search).
Ability to complete tasks and projects with limited supervision.
Attention to detail and follow-through.
Excellent communication skills and ability to work as a team player.
Demonstrated leadership working effectively with engineers, product managers, and other ML practitioners.

Nice To Haves

Experience with Ray, MLFlow, and/or other distributed training frameworks.
In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
Advanced knowledge of Reinforcement Learning or Meta Learning.
Deep understanding of LLM techniques, including Agents, Planning, Reasoning, and related methods.
Experience building and deploying ML models on cloud platforms such as AWS, including tools such as SageMaker and EKS.

Responsibilities

Serve as a subject matter expert on a wide range of machine learning techniques and optimizations.
Provide in-depth knowledge of ML algorithms, frameworks, and techniques.
Enhance ML workflows through advanced proficiency in large language models and related techniques.
Conduct experiments using the latest ML technologies, analyze results, and tune models.
Write code to bring experimental results into production solutions in collaboration with the engineering team.
Own end-to-end Python code development for proof-of-concept, experimentation, and production-ready solutions.
Optimize system accuracy and performance by identifying and resolving inefficiencies and bottlenecks.
Collaborate with product and engineering teams to deliver tailored, science- and technology-driven solutions.
Integrate Generative AI within the ML Platform using state-of-the-art techniques.
Drive decisions that influence product design, application functionality, and technical operations and processes.
Provide mentorship and promote strong engineering practices and continuous learning.