Executive Director Machine Learning Engineer-MLOps

Aumni; Palo Alto (CA); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

JPMorgan Chase in Palo Alto is seeking a Senior MLOps engineer to build and deploy ML models on a modern MLOps stack, collaborating with data scientists on the Recommendation Engine team.

You will implement tuning and reinforcement learning algorithms, run real-time and batch serving, and ensure monitoring, validation, and scalable infrastructure in AWS, helping power personalization across Chase’s consumer banking channels.

Formación

• BS in Computer Science or related field with 10+ years of experience or MS with 6+ years experience.
• Solid knowledge and extensive experience in Python or in cloud computing and AWS.
• Understanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures
• Solid understanding of Transformer models and challenges involved in serving large transformer-based models
• Solid understanding of ML training, especially latest reinforcement learning algorithms such as GRPO and DAPO
• Experience in systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency
• Deep knowledge and passion for data science fundamentals, training and deploying models
• Experience in monitoring and observability tools to monitor model input/output and features stats

Responsabilidades

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage high-volume real-time and batch inference systems, ensuring optimal performance and reliability.
• Implement quantization techniques and deploy open-weight large language models (LLMs) on modern serving stacks such as vLLM on Ray to maximize efficiency and resource utilization.
• Oversee the management and optimization of vector databases to support advanced AI and machine learning applications.
• Establish and maintain comprehensive monitoring and observability pipelines to ensure system health, performance, and rapid issue resolution.
• Collaborate with cross-functional teams to integrate new technologies and continuously improve existing infrastructure.
• Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions.

Conocimientos

Python programming

ML model deployment

Distributed training

Monitoring & observability

CUDA

AWS / cloud computing

Educación

BS in Computer Science or related field

MS in Computer Science or related field

Herramientas

Ray

vLLM

Docker

Kubernetes

AWS

Descripción del empleo

We are looking for a Senior MLOps engineer to work closely with Data Scientists to build and deploy ML models on a modern MLOps stack.

As an Executive Director Machine Learning Engineer on the Recommendation Engine team, you’ll implement fine-tuning and reinforcement learning algorithms on large compute clusters, build and run real-time and batch model serving systems, hyper-parameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment.

Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces.

Job responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage high-volume real-time and batch inference systems, ensuring optimal performance and reliability.
• Implement quantization techniques and deploy open-weight large language models (LLMs) on modern serving stacks such as vLLM on Ray to maximize efficiency and resource utilization.
• Oversee the management and optimization of vector databases to support advanced AI and machine learning applications.
• Establish and maintain comprehensive monitoring and observability pipelines to ensure system health, performance, and rapid issue resolution.
• Collaborate with cross-functional teams to integrate new technologies and continuously improve existing infrastructure.
• Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions.

Required qualifications, capabilities, and skills

• BS in Computer Science or related Engineering field with 10+ years of experience Or MS degree in Computer Science or related Engineering field with 6+ years experience.
• Solid knowledge and extensive experience in Python or in cloud computing and AWS.
• Understanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures
• Solid understanding of Transformer models and challenges involved in serving large transformer-based models
• Solid understanding of ML training, especially latest reinforcement learning algorithms such as GRPO and DAPO
• Experience in systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency, x-region resilient applications
• Deep knowledge and passion for data science fundamentals, training and deploying models
• Experience in monitoring and observability tools to monitor model input/output and features stats
• Solid grounding in engineering fundamentals and analytical mindset

Preferred qualifications, capabilities, and skills

• Experience with recommendation and personalization systems is a plus.
• CUDA experience is a big plus
• Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS]
• Experience with Ray, vLLM, RL libraries such as verl/trl
• Good knowledge of Databases

(i) This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries.

Chase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

Come join us in reshaping the future!