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”™.

• One Team, One Dream We work hand‑in‑hand, building Tinder for our members. We succeed together when we work collaboratively across functions, teams, and time zones, and think outside the box to achieve our company vision and mission.
• Own It We take accountability and strive to make a positive impact in all aspects of our business, through ownership, innovation, and a commitment to excellence.
• Never Stop Learning We cultivate a culture where it’s safe to take risks. We seek out input, share honest feedback, celebrate our wins, and learn from our mistakes in order to continue improving.
• Spark Solutions We’re problem solvers, focusing on how to best move forward when faced with obstacles. We don’t dwell on the past or on the issues at hand, but instead look at how to stay agile and overcome hurdles to achieve our goals.
• Embrace Our Differences We are intentional about building a workplace that reflects the rich diversity of our members. By leveraging different perspectives and other ways of thinking, we build better experiences for our members and our team.

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 Cloud Ops 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 positioned 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.

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

• 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…