Software Engineer L5, Offline Inference, Machine Learning Platform

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

Machine Learning (ML) is core to that experience. From personalizing the home page to optimizing studio operations and powering new types of content, ML helps us entertain the world faster and better.

The Machine Learning Platform (MLP) organization builds the scalable, reliable infrastructure that accelerates every ML practitioner at Netflix. Within MLP, the Offline Inference team owns the batch-prediction layer—enabling practitioners to generate, store, and serve predictions for various models, including LLMs, computer-vision systems, and other foundation models. One of our most critical customer groups today is the content and studio ML practitioners in the company, whose work influences what we create and how we produce movies and shows you see when you log into the Netflix app. 

The Opportunity

We’re looking for a talented Software Engineer L5 to join the newly formed Offline Inference team. You will design, build, and operate next-generation systems that run large-scale batch inference workloads—from minutes to multi-day jobs—while delivering a friction-free, self-service experience for ML practitioners across Netflix. Success in this role means not only building robust distributed systems, but also deeply understanding the ML development lifecycle to build platforms that truly accelerate our users.

What You’ll Do

• Build developer-friendly APIs, SDKs, and CLIs that let researchers and engineers—experts and non-experts alike—submit and manage batch inference jobs with minimal effort, particularly in the domain of content and media

• Design, implement, and operate distributed services that package, schedule, execute, and monitor batch inference workflows at massive scale.

• Instrument the platform for reliability, debuggability, observability, and cost control; define SLOs and share an equitable on-call rotation

• Foster a culture of engineering excellence through design reviews, mentorship, and candid, constructive feedback

Minimum Qualifications

• Hands-on experience with ML engineering or production systems involving training or inference of deep-learning models.

• Proven track record of operating scalable infrastructure for ML workloads (batch or online).

• Proficiency in one or more modern backend languages (e.g. Python, Java, Scala).

• Production experience with containerization & orchestration (Docker, Kubernetes, ECS, etc.) and at least one major cloud provider (AWS preferred).

• Comfortable with ambiguity and working across multiple layers of the tech stack to execute on both 0-to-1 and 1-to-100 projects

• Commitment to operational best practices—observability, logging, incident response, and on-call excellence.

• Excellent written and verbal communication skills; effective collaboration across distributed teams and time zones.

• Comfortable working in a team with peers and partners distributed across (US) geographies & time zones.

Preferred Qualifications

• Deep understanding of real-world ML development workflows and close partnership with ML researchers or modeling engineers.

• Familiarity with cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI, Vertex) or open-source stacks (Ray, Kubeflow, MLflow).

• Experience optimizing inference for large language models, computer-vision pipelines, or other foundation models (e.g., FSDP, tensor/pipeline parallelism, quantization, distillation).

• Open-source contributions, patents, or public speaking/blogging on ML-infrastructure topics.

What We Offer

Our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $100,000 - $720,000.

Netflix provides comprehensive benefits including Health Plans, Ment