Own the technical direction for Bumble’s recommendations platform, evolving the systems that service recommendations for millions of members. Identify the highest-leverage engineering investments across retrieval, ranking, and serving,    while balancing relevance, marketplace health, reliability, and latency.

Lead the design of large-scale distributed systems that power recommendations across multiple teams and domains. From online serving and feature delivery to experimentation and feedback loops, keep them simple, resilient, scalable, and elegant as the platform evolves.

Identify and solve the highest-leverage technical problems across the recommendations stack, clarify ambiguous architectural decisions, and create solutions that help multiple teams move faster.

Raise the bar for engineering quality through technical leadership. Establish architectural principles, engineering standards, observability practices, and operational excellence that improve the quality and maintainability of systems across the organization. 

Multiply the impact of other engineers by mentoring senior engineers, influencing technical direction across teams, and sharing context and judgment on the organization’s most challenging engineering problems.

Drive platform evolution for the long term. Lead foundational initiatives such as service decomposition, recommendation infrastructure, experimentation capabilities, developer tooling, and AI-assisted engineering that enable the organization to move faster over time.

Remain deeply technical through hands-on contribution. Writing and reviewing high-quality code where it creates the greatest leverage. Contributing to critical designs, prototypes, and production code. Rapidly prototyping new ideas and serving as a trusted expert for the most critical parts of Bumble’s recommendations platform.

Typically requires 8+ years of building large-scale backend or distributed systems, with experience delivering complex technical initiatives that span multiple teams.

Deep expertise in designing and operating high-scale distributed systems, with strong experience in modern languages such as Go, Kotlin, Java, or similar, and a track record of building reliable production platforms.

Strong understanding of recommendation systems, ranking architectures, or other large-scale decision systems, including concepts such as retrieval, candidate generation, ranking, feature serving, experimentation, and feedback loops.

Experience building cloud-native systems on Google Cloud Platform (GCP) or comparable public cloud infrastructure, with deep knowledge of scalability, resilience, observability, and operational excellence.

Demonstrated ability to define technical strategy and influence architectural direction across multiple engineering teams through expertise, collaboration, and sound technical judgment rather than organizational authority.

Proven experience partnering closely with Product, Data Science, Machine Learning, and Engineering leadership to translate ambiguous business problems into durable technical solutions.

A track record of mentoring senior engineers, raising engineering standards, and creating leverage by improving systems, tooling, architecture, and the effectiveness of the wider engineering organization.

Strong AI fluency, using modern AI-assisted engineering tools to improve productivity while applying thoughtful human judgment, maintaining high engineering standards, and ensuring member trust remains central to every technical decision.