Staff Software Engineer, Machine Learning Trust & Safety

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 its 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

Were problem solvers, focusing on how to best move forward when faced with obstacles. We dont 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

The Engineering team is responsible for building innovative features and resilient systems that bring people together. We're always experimenting with new features to engage with our members. Although we are a high-scale tech company, the member-to-engineer ratio is very highmaking the level of impact each engineer gets to have at Tinder enormous.

The Trust and Safety team focuses on enhancing account authenticity, mitigating adversarial impact, building public trust, and enabling safer experiences for millions of users globally on Tinder. You will have an opportunity to work on a team that helps support one of our primary missions: ensuring that Tinder remains the safest place to meet new people.

About The Role

As a Staff Software Engineer focused on Trust & Safety, you'll play a pivotal role in shaping the future of Tinder. Our Trust & Safety ML team is responsible for developing machine learning algorithms and systems to keep Tinder members safe and authentic. The team develops cutting-edge technologies in Generative AI, Nature Language Processing (NLP), and Computer Vision (CV) to detect bad actors and content. You'll have a unique opportunity to join a company with a global footprint while working on a team small enough for you to feel the impact each day. With Tinder's global scale and impact, you'll be at the forefront of solving some of the most complex challenges in technology.

Where you'll work

This is a hybrid role and requires in-office collaboration twice per week. This positionis located in Palo Alto, CA.

• Lead the modeling efforts of Tinders Trust and Safety experiences.
• Apply state-of-the-art machine learning techniques, including Generative AI, CV, NLP, etc.
• Lead the research and development of novel algorithms and models, staying at the forefront of advancements in ML technologies.
• Work with big data to improve the accuracy and relevance of models.
• Collaborate with other machine learning engineers, backend software engineers, and product managers to integrate ML models into our systems, improving user experience and driving business objectives.
• Mentor and guide team members, fostering their growth and enabling them to reach their full potential.

• 8+ years of hands-on experience in machine learning, with a proven track record of delivering impactful solutions at scale.
• PhD or MS in machine learning, computer science, statistics, or another highly quantitative field.
• Hands-on experience in the Trust & Safety domain.
• In-depth knowledge of deep neural networks, particularly in the safety domain.
• Proficiency in deep learning frameworks such as PyTorch, TensorFlow, Keras, etc.
• Proficiency in Python, Java, Scala, or similar programming languages.
• Strong decision-making skills with a bias for action and the ability to navigate ambiguity with confidence.
• Proven leadership abilities to inspire and motivate teams to excel and achieve ambitious goals.