Senior ML Engineer — Large-Scale Recommendations

Bumble; Austin (TX); On-site; USD 190,000 - 215,000; Full time

Benefits offered by this job

Health, dental and vision insurance

Unlimited PTO

401k with company match

Annual wellness stipend

Job summary

Bumble Inc. is seeking an accomplished Senior ML Engineer for its Recommendations team. You will own the technical direction of the recommendations platform, guiding retrieval, ranking, serving, and experimentation for millions of members.

You will lead scalable system designs, boost reliability and latency, and mentor engineers while shaping long-term platform evolution with AI-assisted tooling and robust observability.

Qualifications

• Extensive experience designing and operating large-scale distributed systems.
• Deep expertise in Go, Kotlin, Java or similar languages.
• Strong understanding of recommendation systems and ranking architectures.
• Experience deploying cloud-native systems on Google Cloud Platform.

Responsibilities

• Own the technical direction for Bumble’s recommendations platform and identify high-leverage investments.
• Lead the design of large-scale distributed systems across teams and domains.
• Solve high-leverage problems across the recommendations stack and clarify architectural decisions.
• Establish architectural principles, standards, observability, and operational excellence.
• Mentor senior engineers and influence technical direction across teams.
• Drive platform evolution with service decomposition, tooling, and AI-assisted engineering.
• Contribute hands-on code and prototypes for critical parts of the platform.

Skills

Distributed systems design

Go / Kotlin / Java

Recommendation systems

Cloud native / GCP

Technical leadership

Mentoring

AI fluency

Job description

Bumble Inc. is seeking an accomplished Senior ML Engineer for its Recommendations team. You will own the technical direction of the recommendations platform, guiding retrieval, ranking, serving, and experimentation for millions of members.

You will lead scalable system designs, boost reliability and latency, and mentor engineers while shaping long-term platform evolution with AI-assisted tooling and robust observability.