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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineering Manager, Platform Systems - AIMS Engineering - **Company:** Netflix, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Distributed Systems, Machine Learning, Large Language Models, Reliability of Systems, Machine Learning Operations, Automation Anywhere - **Published:** October 14, 2026 - **Apply:** https://startup.jobs/engineering-manager-platform-systems-aims-engineering-netflix-com-10432664 ## About the Role * Significant experience leading platform or infrastructure engineering organizations operating distributed systems at scale. * Strong technical depth in distributed systems. You can engage credibly on architecture, tradeoffs, and design decisions with your most senior engineers. * Hands-on experience building subsystems that support advanced agentic architectures, such as memory, trace, eval, and replay pipelines, or orchestration and routing layers for complex model systems. This should go beyond wiring up an LLM API call. * Experience with ML infrastructure at scale. You understand the operational complexity of online and offline model training pipelines and the platform demands they place on data, compute, and serving infrastructure. * Track record of building high performing engineering teams and developing senior technical talent. * Track record of improving system reliability, reducing infrastructure costs, and accelerating engineering velocity at scale. * Developer productivity as a foundational instinct. You default to removing friction, automating toil, and investing in the tools and systems that make engineers faster. * Operates effectively at the intersection of engineering strategy and organizational leadership; comfortable influencing without direct authority across a large org. * High tolerance for ambiguity and competing priorities in a fast moving environment., * Demonstrated leadership experience building and operating large-scale infrastructure and distributed systems. * Experience developing reliable, scalable machine learning platforms, ideally supporting recommendation, ranking, or personalization systems. * Working knowledge of generative AI alongside traditional machine learning approaches, including recommendation and ranking models. * Understanding of the end-to-end ML lifecycle: model development and training, serving, evaluation, orchestration, monitoring, and integration with a broader ML ecosystem. * Ability to lead teams working across platform infrastructure, agentic AI systems, and recommendation technologies. * Strong technical judgment in balancing system reliability, scalability, model quality, and product needs. ## Description This is a role for a senior engineering leader who can operate at the intersection of technical strategy and organizational execution. You will own the engineering roadmap for Platform Systems, build and scale a high performing team, and set the engineering culture standard for AIMS as a whole. This is not a role for someone who manages from a distance. The problems are technically deep, the dependencies are broad, and the org needs a leader who can credibly engage on distributed systems architecture, steer the design of subsystems that both Netflix's classic and next generation ML systems depend on, and raise the bar on developer productivity across the org at the same time., * Define and own the long term technical strategy for Platform Systems, covering reliability, scalability, capacity management, cost efficiency, the subsystems that support next generation AI workflows, and AI stack modernization, and translate it into a roadmap the team and org can execute against. * Set the engineering excellence standard for AIMS by owning observability, reliability practices, on call hygiene, and engineering norms that raise the bar across the org. * Own the subsystems for observability, evaluation, and tooling that Netflix's next generation ML architecture depends on. Extend the subsystems into everyday engineering workflows, measurably reducing friction in the development lifecycle. * Hire, develop, and retain a team of engineers across a range of seniority levels; create an environment where strong technical contributors grow into senior leaders. * Partner at the director level across AIMS and infrastructure teams, representing Platform Systems in strategic planning and advocating for investments with compounding impact across the org. * Guide the AI stack modernization effort, establishing the migration path and alignment with partner teams across functions, as one part of the broader technical roadmap. * Navigate ambiguity at the intersection of platform engineering, ML infrastructure, and organizational change.