Engineering Manager, Core Applications - AI for Member Systems

The Netflix Mission

At Netflix, our mission is to entertain the world. We are pushing the boundaries of storytelling, global fandom, and making the unimaginable a reality, merging creativity, intuition, and cutting-edge technology.

The Opportunity: AI for Member Systems (AIMS)

Netflix's mission connects millions of members globally with stories they'll love. The AI for Member Systems (AIMS) organization is central to this experience, building and operating the AI systems that power recommendations, personalization, search, discovery, and messaging.

Fast-Paced Innovation in LLMs and Generative AI

Rapid advancements in Large Language Models (LLMs) and Generative AI are transforming personalization and content surfacing. Within AIMS, the AI Applications Core Team is a horizontal applied science and ML engineering team that develops foundational capabilities utilized across various product surfaces. While other AIMS teams focus on specific experiences (Recommendations, Search, Messaging), the Core team builds the shared components that power all of them, including:

• Reward models defining 'good' outcomes for member experiences (engagement, satisfaction, discovery, long-term value).
• Entity/metadata libraries unifying how models reason about content across different surfaces.
• LLM post-training frameworks tailored to personalization use cases.
• Utility optimization systems balancing multiple objectives at scale.

We are seeking a senior engineering leader to grow and lead this team, someone capable of building generalizable tools, APIs, and frameworks for multiple application teams, while balancing near-term product needs with long-term platform investments.

In This Role, You Will

• Lead a senior applied science and ML engineering team that builds foundational personalization capabilities used by multiple product teams.
• Set the vision and roadmap for shared reward models, entity/metadata libraries, embedding utilities, and LLM post-training for personalization.
• Prioritize horizontal investments that create leverage across recommendations, search, messaging, and emerging GenAI experiences.
• Design and evolve APIs, abstractions, and integration patterns for easy adoption by downstream teams, while the Core team iterates on internals.
• Extend and generalize reward models to new content types and surfaces.
• Stand up and tune online utility modeling and optimization layers (e.g., bandits, policy learning, multi-objective optimization) to balance engagement and long-term member value.
• Shape shared post-training and alignment utilities for member-facing LLMs (e.g., supervised fine-tuning, RLHF/RLAIF) used in ranking, discovery, search, and messaging.
• Partner closely with AIMS foundations, product application teams, and ML platform/infra teams to ensure Core capabilities are aligned, integrated, and widely adopted.
• Hire, develop, and retain a diverse, high-caliber team of ML engineers and applied scientists, fostering an environment where senior talent can do their best work.

What We're Looking For

• Experience leading applied ML, ML engineering, or applied science teams working on large-scale personalization, ranking, marketplace optimization, or related decision systems.
• Strong background in applied ML and recommender systems, including rewards, multi-objective optimization, and/or long-term value modeling.
• Demonstrated success driving horizontal or platform-like ML efforts where impact is measured by adoption and leverage across multiple teams.
• Proven ability to design and ship APIs, libraries, and reusable components that product teams can easily adopt and extend.
• Strong communication and influence skills; able to align senior partners across engineering, science, and product.
• Track record of building and leading diverse, high-performing technical teams in a fast-moving, high-autonomy environment.

Preferred Qualifications

• 8+ years in applied ML/science or ML engineering, with 3+ years in a technical leadership or people management capacity.
• Familiarity with modern LLM/GenAI applications and post-training approaches (e.g., fine-tuning, RLHF/RLAIF, evaluation pipelines) in production settings.
• Experience acting as a bridge between foundational/platform teams and product application teams—translating capabilities into usable components while feeding requirements back into foundations.

Compensation and Benefits

Netflix's compensation structure consists solely of an annual salary, with no bonuses. You have the flexibility to choose how much of your compensation you want in salary versus stock options. The compensation range for this role is $523,000.00 - $920,000.00, which varies by 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. Paid leave of absence programs are also offered. Full-time hourly employees accrue 35 days annually for paid time off; full-time salaried employees are immediately entitled to flexible time off. More details about benefits and Netflix's unique culture can be found on their respective pages.

Inclusion and Diversity

Inclusion is a Netflix value. The company strives for a meaningful interview experience for all candidates and offers accommodations for disabilities or other reasons during the hiring process. Netflix is an equal-opportunity employer, celebrating diversity and fostering stronger teams without discrimination based on 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.

Key skills/competency

• Applied Machine Learning
• Machine Learning Engineering
• Personalization Systems
• Recommender Systems
• Large Language Models (LLMs)
• Generative AI
• Multi-objective Optimization
• API Design
• Technical Leadership
• Team Management