Staff ML Engineer: Matching & Recommendations

Bumble; Austin, Northern (TX, KY); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Bumble Inc. in Austin seeks a seasoned machine learning engineer to own the recommendations platform, shaping scalable backend systems and AI-powered features that connect millions of users.

You will lead design of distributed services, collaborate with product and data science teams, and raise engineering quality through strong architectural judgment.

The role emphasizes hands-on coding, mentoring, and long-term platform evolution in a fast-paced environment.

Qualifications

• 8+ years building large-scale backend systems.
• Experience with recommender systems or large-scale decision systems.
• Experience with cloud-native architectures and scalable services.

Responsibilities

• Own the technical direction for Bumble’s recommendations platform, evolving the systems that service recommendations for millions of members.
• Lead the design of large-scale distributed systems that power recommendations across multiple teams and domains.
• Identify and solve high-leverage technical problems across the recommendations stack and guide architectural decisions.
• Raise the bar for engineering quality through architectural principles, observability, and operational excellence.
• Mentor senior engineers, influence technical direction, and share context on challenging engineering problems.
• Drive platform evolution with service decomposition, infrastructure, experimentation, and AI-assisted tooling.
• Remain deeply technical through hands-on contribution with production code and prototypes.

Skills

Distributed systems

Go/Kotlin/Java

AI fluency

GCP

Technical leadership

Job description

Bumble Inc. in Austin seeks a seasoned machine learning engineer to own the recommendations platform, shaping scalable backend systems and AI-powered features that connect millions of users.

You will lead design of distributed services, collaborate with product and data science teams, and raise engineering quality through strong architectural judgment.

The role emphasizes hands-on coding, mentoring, and long-term platform evolution in a fast-paced environment.