Lead Machine Learning Engineer-MLOps

Join JPMorgan Chase as a Lead Machine Learning Engineer MLOps on the Recommendation Engine team, working onsite in Palo Alto, CA. You will build and deploy ML models using a modern MLOps stack, spanning distributed training on GPU clusters, real-time and batch inference, and scalable hyperparameter tuning with production-grade validation. The role offers a competitive base salary in the range of USD 164,350 to 260,000 per year.

Responsibilities

• Design, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to enable scalable ML workflows.
• Develop pipelines for high throughput real time as well as batch inference, prioritizing performance and reliability.
• Implement quantization techniques and deploy large language models to improve efficiency across specific GPU architectures.
• 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, performance, and rapid issue resolution.
• Collaborate with product, architecture, and other engineering teams to integrate new technologies and continuously improve existing infrastructure.
• Partner with cross functional teams to define scalable and performant technical solutions.

Requirements

• BS in Computer Science or related Engineering field with 6+ years of experience.
• MS in Computer Science or related Engineering field with 4+ years of experience.
• Strong proficiency in Python and cloud computing, preferably AWS.
• Understanding of quantization techniques such as PTQ and AWQ used to accelerate LLM inference on specific GPU architectures.
• Experience in systems engineering fundamentals including caching, CUDA, autoscaling, high throughput, low latency, and cross-region resilience.
• Solid knowledge and passion for data science fundamentals, including training and deploying models.
• Experience with monitoring and observability tools to track model inputs/outputs and feature statistics.
• Operational experience with big data and ML tools such as Ray, DuckDB, Spark, and with training/inference systems like Ray and vllm/SGLang.
• Strong engineering fundamentals and an analytical mindset.

Technologies

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

Benefits

• Base salary determined by role, experience, skills, and location.
• Commission-based pay and discretionary incentive compensation, paid in cash and/or equity, recognizing individual contributions.
• Comprehensive health care coverage.
• On site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.