Software Engineer 5 – Offline Inference, AI Platform

Job Description

Software Engineer (Machine Learning Platform)

Netflix

Job Details

• Team: Machine Learning Platform (MLP) - Offline Inference
• Salary: $466,000.00 - $750,000.00 annually

About the Role

At Netflix, our mission is to entertain the world. 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.

We’re looking for a talented Software Engineer 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.

What You’ll Do

• Build developer-friendly APIs, SDKs, and CLIs that let researchers and engineers 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.

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.

Compensation & Benefits

• Compensation Structure: Consists solely of an annual salary; no bonuses. You choose each year how much of your compensation you want in salary versus stock options.
• Comprehensive Benefits: Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts.
• Additional Benefits: Family-forming benefits, and Life and Serious Injury Benefits.
• Paid Time Off: Full-time salaried employees are immediately entitled to flexible time off.

Culture & Inclusion

Netflix is a unique culture and environment. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.