Lead Machine Learning Engineer-MLOps

Benefits and culture

Become part of JPMorganChase in Palo Alto, onsite, as a Lead Machine Learning Engineer - MLOps on the Recommendation Engine team. You will work with a modern MLOps stack to design, train, and deploy ML models, including distributed training on GPU-enabled clusters, real-time and batch inference, and production monitoring and validation. The role offers a competitive salary and a comprehensive benefits package, reflecting a culture that prioritizes collaboration with product, architecture, and engineering peers to deliver scalable, impactful solutions.

• Salary: USD 164,350 - 260,000 per year
• Commission-based pay and/or discretionary incentive compensation, paid in cash and/or forfeitable equity
• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching

In this role, you will lead the development, deployment, and ongoing operation of ML models on a robust MLOps platform. Expect collaboration across teams to implement scalable, efficient solutions, with a focus on performance, reliability, and continuous improvement.

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable ML workflows
• Develop and manage pipelines for high-throughput real-time inference as well as batch inference, prioritizing performance and reliability
• Implement quantization techniques and deploy large language models (LLMs) to maximize efficiency and resource utilization
• Oversee the management and optimization of vector databases to support advanced AI and ML applications
• Establish and maintain comprehensive monitoring and observability pipelines to ensure system health 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

Requirements

• MS in Computer Science or related Engineering field with 4+ years of experience
• BS in Computer Science or related Engineering field with 6+ years of experience
• Solid knowledge and extensive experience in Python and in cloud computing, preferably AWS
• Understanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures
• 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
• Operational experience in big data/ML tools such as Ray, DuckDB, Spark and in training/inference systems such as Ray, vllm/SGLang
• Solid grounding in engineering fundamentals and analytical mindset

Technologies

• Python
• AWS
• CUDA
• PTQ
• AWQ
• Ray
• DuckDB
• Spark
• vllm
• SGLang
• Docker
• Kubernetes
• ECS
• Airflow
• Kubeflow
• vector databases