Executive Director Machine Learning Engineer-MLOps
Salary not listed • Palo Alto, CA, United States • Full-time
Posted 8d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
Job Location
Palo Alto, CA, United States
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
As an Executive Director Machine Learning Engineer on the Recommendation Engine team, you will implement fine-tuning and reinforcement learning algorithms on large compute clusters. You will build and run real-time and batch model serving systems, manage hyper-parameter tuning at scale, and oversee model monitoring and production validation. This role is vital for model development, testing, and deployment in a well-managed, controlled environment, supporting high throughput, low latency applications that leverage state-of-the-art machine learning architectures deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels.
Responsibilities
- Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters.
- Develop and manage high-volume real-time and batch inference systems.
- Implement quantization techniques and deploy open-weight large language models (LLMs) on modern serving stacks like vLLM on Ray.
- Oversee the management and optimization of vector databases.
- Establish and maintain comprehensive monitoring and observability pipelines.
- Collaborate with cross-functional teams to integrate new technologies and improve existing infrastructure.
- Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions.
Requirements
- BS in Computer Science or related Engineering field with 10+ years of experience, or MS degree with 6+ years experience.
- Solid knowledge and extensive experience in Python or cloud computing and AWS.
- Understanding of quantization techniques (PTQ, AWQ) for LLMs.
- Solid understanding of Transformer models and challenges in serving large transformer-based models.
- Solid understanding of ML training, especially reinforcement learning algorithms (GRPO, 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 with monitoring and observability tools for model input/output and feature statistics.
- Solid grounding in engineering fundamentals and analytical mindset.