Machine Learning Scientist 5 - Multi-modal Algorithms for Games

At Netflix, the Studio Media Algorithms team leads algorithmic innovation to enhance and support the creation of entertainment content, including games. This role is embedded within that team and collaborates closely with a specialized Games Studio R&D team. Role We are seeking a Machine Learning Scientist to lead the research and development of Large Language Models (LLMs), Vision‑Language Models (VLMs), and multi‑modal foundations and solutions for games. This role is defined by a mandate for inference efficiency; you will not only build and fine‑tune state‑of‑the‑art models but also lead the algorithmic innovation required to make them viable in terms of cost, latency, and quality across a variety of cloud and edge devices. You will work in close partnership with our Machine Learning Engineers to bridge the gap between "research‑grade" models and high‑performance deployment, focusing on algorithmic optimization—ensuring that our language, visual, and audio models are architecturally optimized for real‑time interaction and efficiency. Responsibilities Model Adaptation & Alignment: Design and own the fine‑tuning and alignment of LLMs and VLMs in PyTorch, leveraging modern preference learning and reinforcement learning to enhance reasoning, tool‑use, and agentic workflows for interactive game systems. Algorithmic Model Optimization: Lead efforts in model compression—specifically knowledge distillation, structural pruning, and architectural refinement—to create efficient variants of large models that meet strict latency, cost, and quality constraints. Generative Visuals & Diffusion: Develop and optimize Diffusion‑based models for Image, Video, and 3D generation, including distillation and efficiency techniques for viable game‑time performance. Pragmatic Model Integration: Strategically evaluate and integrate SOTA open‑source and commercial models while building internal "layers," adapters, and enhancements to fill gaps in creative control. Multi‑modal Interaction: Optimize and integrate audio (ASR/TTS), language, and vision models to enable low‑latency, cross‑modal reasoning and interaction. Qualifications Multi‑modal Architecture Expertise: Strong foundation in deep learning architectures, with deep expertise in Transformers and Diffusion architectures powering LLMs, VLMs, and generative visuals, including their specific performance bottlenecks. Optimization Specialist: Proven track record in algorithmic model optimization (e.g., distillation, quantization‑aware training, or pruning) to reduce FLOPs and memory footprint. Data‑Centric Mindset: Skilled in data cleaning, curation, and the creation of synthetic data for complex evaluation and training pipelines. Pragmatic Builder: Ability to prioritize impact by deciding when to use commercial APIs/OSS weights versus when to invest in proprietary R&D to solve efficiency or quality problems. Programming: Expert proficiency in Python and deep learning frameworks (such as PyTorch); ability to collaborate with engineering on low‑level performance constraints. Bonus Experience: Prior experience optimizing models for heterogeneous hardware (Mobile, Cloud GPU, and custom edge devices). Expertise in audio‑visual multimodal models and video generation. Compensation and Benefits 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. The range for this role is $466,000.00 - $750,000.00. 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. EEO Statement 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. #J-18808-Ljbffr Netflix