Sr. Software Engineer, Machine Learning Infrastructure

Our Mission:

Launched in 2012, Tinder® revolutionized how people meet, growing from 1 match to one billion matches in just two years. This rapid growth demonstrates its ability to fulfill a fundamental human need: real connection. Today, the app has been downloaded over 630 million times, leading to over 97 billion matches, serving approximately 50 million users per month in 190 countries and 45+ languages - a scale unmatched by any other app in the category. In 2024, Tinder won four Effie Awards for its first-ever global brand campaign, “It Starts with a Swipe”™

What You'll Do:

• Build and evolve robust, scalable ML infrastructure that supports ML engineers across all Tinder business domains
• Set and drive the long-term technical direction for Tinder’s ML infrastructure
• Design, build, and operate production-grade ML serving infrastructure for ML models using Ray Serve and Triton
• Develop and maintain robust serving infrastructure specialized for serving large language models (LLMs) in-house
• Develop efficient ML serving platform using Ray Serve and Triton
• Build the foundation of Tinder’s feature store using Databricks and internal tooling
• Own infrastructure projects end to end—from design and implementation to adoption and measurable impact.
• Partner closely with ML Engineers, ML Software Engineers, and CloudOps to ensure infrastructure directly enables better models and faster iteration
• Establish and propagate best practices in ML infrastructure, data engineering, and model serving
• Mentor and support junior engineers, raising the technical bar across the team

What You'll Need:

• Bachelor’s degree in Computer Science, Engineering, Technology, or a related field.
• 5+ years of experience building or operating ML platforms, including training, serving, feature management, or experimentation systems.
• Hands-on experience designing, building, or running feature stores at scale.
• Strong software engineering fundamentals, with proficiency in Python and at least one of Java, Scala, Go, or a similar language.
• Practical experience with ML serving platforms such as Triton, Ray Serve, or Seldon.
• Solid grasp of core machine learning concepts, including model training, evaluation, validation, and performance measurement.
• Proven ability to lead cross-functional initiatives and work effectively across ML, infrastructure, and product teams
• Deep experience in distributed systems, cloud infrastructure, and MLOps, with hands-on exposure to transformers and modern deep learning architectures
• Ability to bridge the gap between cutting-edge ML research and reliable, production-grade systems