Lead Machine Learning Engineer-MLOps Vice President
Salary not listed • Palo Alto, CA, United States • Full-time
Posted 3d ago
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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
We are seeking a Lead Machine Learning Engineer with a focus on MLOps to join our Recommendation Engine team. In this role, you will be instrumental in building and maintaining sophisticated pipelines for distributed model training on large compute clusters, as well as for batch and real-time model serving. You will also manage hyperparameter tuning at scale, model monitoring, and production validation within a well-managed, controlled environment. Our product, Personalization and Insights, delivers high-throughput, low-latency applications powered by state-of-the-art machine learning architectures deployed on AWS. These applications enhance personalized experiences across Chase Consumer & Community Banking channels, integrating traditional banking services with offerings in Travel, Merchant Offer Shopping, and Dining.
Responsibilities
- Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters.
- Develop and manage pipelines for high-throughput, real-time inference and batch inference.
- Implement quantization techniques and deploy large language models (LLMs) for efficiency.
- 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 infrastructure.
- Partner with product, architecture, and engineering teams to define scalable technical solutions.
Requirements
- BS in Computer Science or related Engineering field with 6+ years of experience, or MS degree with 4+ years experience.
- Solid knowledge and extensive experience in Python.
- 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.
- Operational experience in big data/ML tools such as Ray, DuckDB, Spark.
- Solid grounding in engineering fundamentals and analytical mindset.
- Experience with containers (Docker) and orchestration systems (Kubernetes, ECS).
- Experience with DAG orchestration (Airflow, Kubeflow).
- Good knowledge of Databases.
- Solid fundamentals in cloud computing, preferably AWS.