Overview Leader for Netflix's Title Launch Management data science and engineering team, driving automation, evaluation pipelines, and safe, scalable launch processes. You will shape data infrastructure and AI-driven decisioning across global title launches, partnering with product, operations, and engineering to deliver fast, reliable launches. The role combines people leadership with hands-on technical oversight to reduce risk and improve performance at scale. Join a mission-driven team that blends creativity with cutting-edge technology to connect content with audiences worldwide.
Compensation / Benefits- Health plans
- Mental health support
- 401(k) with employer match
- Stock option program
- Disability programs
- Health Savings Account and Flexible Spending Accounts
Responsibilities- Oversee end-to-end initiatives advancing Netflix's title launch strategy across the content slate
- Develop scalable evaluation pipelines and safety guardrails to monitor launch speed, coverage, and risk
- Design feedback loops and data infrastructure to feed high-quality real-user data into evaluation and training
- Coach, hire, and develop a team of ML scientists, analytics engineers, and data engineers
- Collaborate with Product Management, Launch Operations, Partner Integration Managers, and engineering teams to shape strategy and drive rollout plans
- Cultivate partnerships across product, engineering, and operations; communicate complex ideas clearly to diverse audiences
- Foster ownership and reliable delivery against roadmap; act as a trusted technical advisor balancing business goals and engineering practices
- Create an inclusive, empowered environment that values diverse perspectives
Key requirements- 3+ years of direct management experience shipping production-grade AI/ML software with measurable impact
- Proficiency in software engineering fundamentals and LLMs, RAG, and agentic architectures
- Strong track record designing and implementing AI/ML evaluation pipelines and safety guardrails
- Familiarity with ML evaluation methodologies (offline metrics, online experiments, human evaluation)
- Experience introducing automation into established workflows and managing human-in-the-loop handoffs
- Open communication and ability to mentor and give feedback
- Strong product mindset translating goals into team priorities
- Effective cross-functional collaboration and data-driven decision making
- Clear communication with team, stakeholders, and global partners
- leadership and people development
- cross-functional collaboration
- vision-to-execution orientation
- AI/ML lifecycle proficiency (MLOps platforms, PyTorch, TensorFlow, Metaflow)
- LangGraph, DSPy, Agent SDK (agentic architectures)
- vector databases
