Executive Director, ML Engineer (MLOps) - New York City

Salary: $61,000 - 101,000 per year Requirements:

• Bachelors degree in Computer Science or a related engineering field and 10+ years of experience, or a masters degree in the same fields and 6+ years of experience.
• Strong knowledge and extensive experience with Python or cloud computing and AWS.
• Understanding of LLM quantization methods such as PTQ and AWQ for accelerating inference on specific GPU architectures.
• Strong understanding of Transformer models and the challenges of serving large Transformer-based models.
• Strong understanding of machine learning training, especially recent reinforcement learning algorithms such as GRPO and DAPO.
• Systems engineering experience covering caching, CUDA, autoscaling, high throughput, low latency, and cross-region resilience.
• Deep knowledge of data science fundamentals and model training and deployment.
• Experience with monitoring and observability tools for tracking model inputs, outputs, and feature statistics.
• Strong engineering fundamentals and an analytical mindset.
• Preferred: Experience with recommendation and personalization systems.
• Preferred: CUDA experience.
• Preferred: Experience with Docker and container orchestration platforms such as Kubernetes and ECS.
• Preferred: Experience with Ray, vLLM, and reinforcement learning libraries such as verl or trl.
• Preferred: Good knowledge of databases.
• Employment is contingent on review of criminal conviction history, including pretrial diversions or program entries, under Section 19 of the Federal Deposit Insurance Act.

• Build, deploy, and maintain pipelines for distributed training on GPU-enabled clusters.
• Develop and manage high-volume real-time and batch inference systems, ensuring performance and reliability.
• Apply quantization techniques and deploy open-weight large language models on serving stacks such as vLLM on Ray to improve efficiency and resource utilization.
• Manage and optimize vector databases for AI and machine learning applications.
• Create and maintain monitoring and observability pipelines to track system health and performance and resolve issues quickly.
• Collaborate with cross-functional teams to integrate technologies and improve infrastructure.
• Partner with product, architecture, and engineering teams to define scalable, high-performance technical solutions.
• Work closely with Data Scientists to build and deploy machine learning models on a modern MLOps stack.
• Implement fine-tuning and reinforcement learning algorithms on large compute clusters; support model serving, hyperparameter tuning, monitoring, production validation, development, testing, and deployment.

• AI
• AWS
• Cloud
• CUDA
• Docker
• Fine-tuning
• Support
• Kubernetes
• LLM
• Machine Learning
• MLOps
• Model Serving
• Model Training
• Python
• vLLM

More:

We are collaborating with hackajob to connect professionals with J.P. Morgan. In this Executive Director Machine Learning Engineer role on the Recommendation Engine team, we support Personalization and Insights products: high-throughput, low-latency applications deployed on AWS that power personalized experiences across Chase Consumer & Community Banking, including banking, travel, merchant offers, shopping, and dining. Chase is a financial services firm serving households and small businesses. Our Consumer & Community Banking division provides personal banking, credit cards, mortgages, auto financing, investment advice, small business loans, and payment processing. The CCB Data & Analytics team applies data across Chase to support business advantages and customer value and protection, partnering closely with Technology on data and analytics infrastructure. We offer competitive compensation and, for eligible roles, incentives and benefits that may include health coverage, wellness centers, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We value diversity and inclusion and provide reasonable accommodations for religious practices and disability-related needs.

last updated 42 week of 2026