Staff ML Engineer - Large-Scale Recommendations

Bumble Inc.; Austin (TX); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

Bumble Inc. is shaping the future of online connections with AI-driven recommendations and a scalable backend to power millions of interactions.

This role leads architectural decisions, mentors engineers, and drives platform evolution, balancing performance, reliability, and developer productivity. Ideal candidates bring 8+ years building distributed systems, cloud-native experience on GCP, and a strong track record in ML/AI-enabled engineering.

Formación

• 8+ years of building large-scale backend or distributed systems.
• Deep expertise in designing and operating high-scale distributed systems with modern languages (Go, Kotlin, Java).
• Strong understanding of recommendation systems or large-scale decision systems.
• Experience building cloud-native systems on public clouds (GCP).
• Ability to define technical strategy and influence architectural direction across teams.
• Experience partnering with Product, Data Science, ML and Engineering leadership.

Responsabilidades

• Own the technical direction for Bumble’s recommendations platform across retrieval, ranking, and serving.
• Lead the design of large-scale distributed systems powering recommendations for multiple teams.
• Identify and solve high-leverage technical problems across the recommendations stack.
• Raise engineering quality through architecture, standards, observability, and operational excellence.
• Mentor senior engineers and influence technical direction across teams.
• Drive platform evolution through foundational initiatives like service decomposition and tooling.
• Contribute hands-on by writing and reviewing high-quality production code.

Conocimientos

Distributed systems

Go/Kotlin/Java

GCP

AI fluency

Descripción del empleo

Bumble Inc. is shaping the future of online connections with AI-driven recommendations and a scalable backend to power millions of interactions.

This role leads architectural decisions, mentors engineers, and drives platform evolution, balancing performance, reliability, and developer productivity. Ideal candidates bring 8+ years building distributed systems, cloud-native experience on GCP, and a strong track record in ML/AI-enabled engineering.