Netflix is one of the worlds leading entertainment services, with 283 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/Artificial Intelligence powers innovation in all areas of the business, from helping members choose the right title for them through personalization, to better understanding our audience and our content slate, to optimizing our payment processing and other revenue-focused initiatives. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.More recently, fast-paced innovation in large language models (LLMs) has greatly helped advance state-of-the-art technology in many areas of personalization, including search and recommendation experiences. <h3></h3><h3>The Opportunity</h3>The Model Serving Systems team provides the computational platform on which we build nearly all our consumer and studio-facing ML/AI applications. We provide all the building blocks to serve ML models at scale, including a real-time model inference and serving platform, foundational abstractions that ensure consistency between online and offline systems, and more. Additionally, as we expand to enable LLM innovation in numerous areas of personalization, we’re building model serving infrastructure for LLMs and other large foundation models. Were expanding our model serving systems to meet the evolving needs of the evolving AI landscape. ​​We are looking for strong engineers to develop and expand our compute infrastructure to support the growing AI needs, enable the application of ML in new business areas, and drive ML/AI innovation across Netflix. Our systems power some of Netflixs most business-critical models, and we need you to take our ML/AI initiatives to the next level. You will play a highly cross-functional role, partnering with other engineers, product managers, machine learning engineers, and data/research scientists.If you have a passion for building scalable, robust systems, are interested in pushing the envelope in applying ML algorithms, and operate in a critical part of the stack that strongly influences what our customers see on their screens, then we want to talk to you.<h3></h3><h3>You may enjoy working with us if:</h3><ul><li>You strive to embrace best practices and are curious about discovering new and better ways to solve problems.</li><li>You are self-driven and highly motivated to deliver top-tier solutions while learning from and collaborating with Stunning Colleagues.</li><li>You are strongly motivated to pick up new domains and ship high-quality, extensible code.</li><li>You are excited to work in a multidisciplinary environment (engineering, algorithms, data engineering/science, product experimentation).</li><li>You are comfortable working in a team with peers and partners distributed across (US) geographies &amp; time zones.</li></ul><h3></h3><h3>We would love to work with you if:</h3><ul><li>You have experience building high-traffic distributed services and infrastructure for online ML model inference and are familiar with supporting large-scale ML models focusing on high availability and performance.</li><li>You understand scalable model-serving solutions for generative models and LLMs, with skills in reducing latency and costs, and can solve bottlenecks to streamline research-to-production workflows.</li><li>You are proficient in object-oriented programming (preferably Java) and demonstrate engineering excellence in production hosting, including performance tuning, deployment management, and capacity planning.</li><li>You are familiar with deploying ML models using tools like Triton Inference Server, TensorRT, Docker.</li><li>You are experienced working with the public cloud like AWS, Azure, or GCP.</li><li>You are a proactive communicator who promotes best practices in observability and logging. </li><li>You have a BS/MS in Computer Science, Applied Math, Engineering, or a related field.</li></ul>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 - $720,000KNetflix 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 <a href="https://jobs.netflix.com/work-life-philosophy" target="_blank">here</a>.Netflix has a unique culture and environment.  Learn more <a href="https://jobs.netflix.com/culture" target="_blank">here</a>. <a target="_blank" href="https://about.netflix.com/en/inclusion">Inclusion</a> 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.