Software Engineer, ML Infra – Distributed Systems (Staff & Principal)

🕒 April 1

🏢🏡 San Francisco – Hybrid

💵 $227.2k - $417k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Tubi

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

📱 Media

Media • Entertainment • Advertising

Tubi is a free, ad-supported streaming service offering a wide selection of movies and TV shows. It features a diverse array of content categories including comedy, romance, drama, horror, and action. Tubi provides a platform for both classic and contemporary films, appealing to a wide audience by keeping its service free and easily accessible across various devices. With a focus on providing entertainment without subscription fees, Tubi partners with other businesses for personalized advertising and viewer engagement.

📋 Description

• Design and build scalable, high throughput, and low latency distributed systems using Scala • Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration • Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art. • Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary • Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc. • Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi.

🎯 Requirements

• Experience designing and building scalable, distributed systems in any modern backend language (e.g., Scala, Java, Python, Go, C++); experience with Scala or JVM based language is a plus. • Strong experience with AWS or an equivalent cloud platform • Experience building online microservices at scale with low latency serving • Experience with both SQL (e.g. Postgres) and NoSQL databases (e.g. Cassandra), message brokers (e.g. Kafka), and caches (e.g. Redis) • Experience with containerization technologies, such as Docker or Kubernetes • Led the response and resolution efforts for multiple major, large-scale incidents

🏖️ Benefits

• Various benefits including medical/dental/vision, insurance, 401(k) plan, paid time off, wellness reimbursement • Annual discretionary bonus • Long-term incentive plan