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 Values

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.

The 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.

The Role

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

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

$220,000 – $250,000 a year

Factors such as scope and responsibilities of the position, candidate’s work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting.

Commitment to Inclusion

At Tinder, we don’t just accept difference, we celebrate it. We strive to build a workplace that reflects the rich diversity of our members around the world, and we value unique perspectives and backgrounds. Even if you don’t meet all the listed qualifications, we invite you to apply and show us how your skills could transfer. Tinder is proud to be an equal opportunity workplace where we welcome people of all sexes, gender identities, races, ethnicities, disabilities, and other lived experiences. Learn more here: https://www.lifeattinder.com/dei

If you require reasonable accommodation to complete a job application, pre-employment testing, or a job interview or to otherwise participate in the hiring process, please speak to your Talent Acquisition Partner directly.

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