The posting
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.
Netflix is on the journey to rebuild the AI/ML model serving platform and infrastructure that turns trained models into served predictions across real-time, async, and agentic workloads, at a scale few companies operate at. Supporting many production clusters, from classical to foundation models, spanning latencies from milliseconds rankers to LLM calls, and powering everything members see from homepage ranking to search, messaging, and internal AI productivity tools.
We are re-building a new model serving platform from the ground up, designed from first principles to meet industry standards. It unifies our in-house inference infrastructure, third-party model hosting, and external LLM providers under a single serving, routing, and experimentation layer. Teams get one consistent way to deploy, route, and A/B test any model, regardless of where it runs.
Re-engineering how GPU capacity is provisioned and used. During off-peak periods, the same fleet that serves latency-critical production traffic is repurposed to run open-weight models for internal coding and developer productivity use cases.
This role sits at the center of Netflix’s GenAI and Foundation Model serving strategy, with real R&D scope on frontier AI infrastructure. You will tackle the efficiency problems that most directly drive cost per inference and capacity: disaggregated serving, intelligent routing across models and providers, speculative decoding, prompt caching, and multi-GPU inference. These are the levers that turn GPU spend into measurable business value.
You will lead the technology behind some of Netflix’s largest bets, embedding AI capabilities into member-facing products at global scale. You will also build durable, resumable execution for long-running agentic workloads. Online and asynchronous/ Near-Real-Time inference is projected to grow fast in 2028, so getting this foundation right now is what keeps costs sustainable and reliability high as demand scales.
This is a rare very senior level opportunity to set and own the technical direction for a platform at the intersection of massive production scale, aggressive cost/ utilization engineering, and genuinely unsolved problems in GenAI serving by leading the technical architecture and directions for a growing engineering organization.
Looking for deep expertise in distributed systems and high-performance RPC, GPU/CPU inference optimization, control planes, and experimentation infrastructure, plus fluency with modern LLM serving stacks and a track record of architecting systems that hold together under both extreme scale and rapid technology change.
Qualifications and Expectations
To succeed in this role, you will need:
- Have a BS/MS/PhD in Computer Science, Applied Math, Engineering, or a related field. With relevant experience in the AI/ ML Infrastructure and distributed systems for 12+ years.
- Influence without authority to guide architectural decisions and alignment across multi-teams through trust, data, and demonstrated expertise.
- Shape long-term technical roadmaps (typically looking 2 to 3 years ahead) and ensure scalability, maintainability, and business alignment.
- Cross-Functional Partnership: Work closely with product managers, operations, and executive stakeholders to balance technical trade-offs with business priorities.
- Clear Communication skill to translate dense technical concepts for non-technical stakeholders and produce clear documentation, architecture decision records (ADRs), and proposals.
- Have experience with high-traffic distributed services and infrastructure for AI/ ML model inference, with a focus on high availability, security, and performance.
- Have experience building scalable model-serving solutions for Foundation Models that powers generative and predictive AI applications with practical skills in reducing latency and costs.
- Familiar with deploying AI/ ML models using platform and solutions such as NVIDIA Triton Inference Server, vLLM, or SGLang, and scaling them via Kubernetes.
- Have experience working with public cloud infrastructure providers such as AWS, Azure, or GCP.
Preferred Qualifications:
- Have open-source contributions to projects in the AI/ML, distributed systems, or infrastructure space.
- Hands-on experience with building AI models, and have explored optimization techniques for AI models, such as quantization, model compression, or model pruning.
- Proficient in leveraging AI coding tools in software development.
- Have experience working with model gateway and intelligent routing for choosing the right AI models based on context of the requests.
- Exposure to deploying multi-adapters model serving, disaggregated prefill/decode, or large-scale KV cache management.
- Working with evaluation and benchmarking tools for LLMs and AI Agents.
- Knowledge of GPU topology, NVIDIA CUDA programming, and machine learning compilers.
Key technologies & ecosystem
- Deep Learning frameworks: Pytorch, TensorFlow, JAX, Hugging Face Transformers
- Inference Engines and Runtimes: vLLM, SGLang, TensorRT-LLM, TGI (Text Generation Inference), DeepSpeed-Inference, NVIDIA Dynamo
- Hardware and Low-Level: CUDA, NCCL, and profiling tools like NVIDIA Nsight.
To learn more about our AI Platform, you can review the relevant talks/blog posts on the Netflix AI Platform Research website.Place holder...coming soon
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 $890,000.00 - $1,690,000.00. 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.
