Software Engineer 5 - Offline Inference, Machine Learning Platform

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. 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 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. Generally, 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 $466,000.00 - $750,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. 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. Job is open for no less than 7 days and will be removed when the position is filled. At Netflix, we want to entertain the world. Whatever your taste, and no matter where you live, we give you access to best-in-class TV series, documentaries, feature films and games. Our members control what they want to watch, when they want it, in one simple subscription. We’re streaming in more than 30 languages and 190 countries, because great stories can come from anywhere and be loved everywhere. We are the world’s biggest fans of entertainment, and we’re always looking to help you find your next favorite story. Apply tot his job Apply To this Job