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”™

Our ML Infrastructure team builds the platforms, tooling, and services that power applied machine learning across Tinder. We provide the foundations for training, deploying, and monitoring large-scale ML systems that impact core experiences like Recommendations, Trust & Safety, Profile, Chat, Growth, and Revenue optimization. 

In this position, we are looking for a Senior Machine Learning Infra Engineer who can build foundational ML infra, including feature store and efficient serving platform (LLM serving). Just to give you a high level overview of the team, ML team at Tinder is organized into three groups with different roles:

• Machine Learning Engineers who focus on modeling and algorithmic innovation.
• Machine Learning Infrastructure Engineers (this role) who build the platforms and tools that enable scalable training, serving, and feature management.
• Machine Learning Software Engineers who bridge the gap between research and production — delivering machine learning models into real-world Tinder features at scale.

In this role, you’ll partner closely with ML engineers, ML software engineers, and the CloudOps team to increase the ML organization’s overall velocity by building and evolving feature store infrastructure and enabling large-scale model serving. You’ll own projects end to end, working in tight alignment with ML teams to ensure infrastructure improvements are actually adopted and drive real impact. ML team at Tinder is driving significant business impact across domains and this infrastructure team is uniquely position to amplify that impact across the domains. For example, enabling more efficient and scalable model serving directly unlocks larger models across the domains, which can lead to consistent metric improvements across multiple product surfaces.

Where you'll work

This is a hybrid role and requires in-office collaboration three days per week. This position is located in Palo Alto, CA

In this role, you will:

• 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

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