Staff Machine Learning Engineer, Platform & Production

Warner Bros. Discovery•San Francisco, CA

2d•$132,300 - $245,700

About The Position

We are looking for a passionate Staff Machine Learning Engineer to bridge the gap between cutting-edge ML algorithm development and production grade, scalable software engineering for our search and personalization systems. In this role, you will be the technical leader of our engineering rigor, design the core architecture, establish engineering best practices, create scalable MLOps pipelines, and design a codebase that allows the team to iterate, deploy, and quickly experiment in production. You will act as a force multiplier, elevating the software engineering capabilities of the entire ML team.

Requirements

8+ years of industry experience, with 4+ years as tech lead experience (preferred)
Deep practical knowledge in designing scalable, production-grade Search or Recommendation systems architecture
Knowledge of large-scale distributed systems, architecture, APIs, and databases.
Solid practical understanding in modern machine learning lifecycle, common pain-points, and research and production environments.
Staff level experience building production systems with expertise in Python/Java, containerization, and cloud platforms.
Hands-on experience with MLOps tools and frameworks (MLflow, Kubeflow, Sagemaker, etc), or building internal systems.
Familiarity with machine learning algorithms (in particular for recommender systems) is a strong plus

Responsibilities

Recommendation & Search model training architecture: Architect, build, and scale recommendation systems powering personalization and search experiences across our streaming platforms.
Codebase Architecture: Design modular, scalable ML repositories built for rapid iteration, fast deployment, and production experimentation.
Engineering Excellence: Establish coding standards, testing frameworks, code review workflows, and CI/CD pipelines using open-source cloud technologies for ML and DS workstreams.
Production Velocity: Build infrastructure abstractions and components that allow seamless transition from model training to large-scale serving.
Cross-Functional Delivery: Collaborate with other ML engineers, data scientists and product managers, to drive complex ML projects from conception to completion.
Strategic Planning: Partner with product and engineering leadership to define, plan, and deliver against long-term strategic goals.
Culture & Mentorship: Mentor and influence engineers across organizations by demonstrating high-quality work, advocating for the customer, and fostering an innovative, engineering-driven culture.