Staff Software Engineer, Machine Learning

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.

Team Introduction

The Tinder ML team drives impact across nearly every core domain of the product — Recommendations, Trust & Safety, Profile, Chat, Growth, and Revenue optimization. Our mission is to apply machine learning to enhance user experiences, foster trust, and accelerate business growth across Tinder’s ecosystem.

About the Role

In this position, we are looking for a highly motivated and experienced Staff-level Machine Learning Engineer who operates at the boundary of multiple domains and partners closely with the Director of Engineering. This is a senior individual contributor role for someone who thrives in ambiguity, can step into complex or struggling initiatives across domains, and help drive them back on track through hands-on technical leadership. While most ML engineers are embedded within a single pod, this role is intentionally cross-cutting. You will work across domains (for example, Trust & Safety and Profile, or Recommendations and Growth), identifying gaps, unblocking execution, and setting technical direction where ownership is unclear, or problems span multiple teams.

In this role, you will operate in multiple modes depending on the needs of the organization. At times, you will act as a hands-on technical leader, driving complex initiatives that span multiple teams. At other times, you will embed with a single pod to provide deep technical support and help unblock execution. You will also act as a strategic thought partner to engineering managers—helping shape direction and identify gaps, risks, and opportunities across ML systems that cut across domains. In addition, you will work closely with domain tech leads as a cross-domain advisor, helping bridge architectures, data, and decisions across teams.

This role offers a unique opportunity to gain deep, end-to-end understanding of how machine learning operates across every corner of Tinder’s product and bring strategic view into the team together with engineering manager, while having a direct hands-on impact as IC.

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

• Lead and execute cross-cutting machine learning initiatives that span multiple ML domains, especially where ownership is unclear or problems cut across teams.
• Partner closely with the Director of Engineering to identify the opportunities and set the technical direction of ML team.
• Step into complex or struggling projects, diagnose issues quickly, and help bring them back on track through hands-on technical leadership and execution.
• Embed with individual pods as needed to provide deep technical support, unblock delivery, and raise the quality bar for ML systems and implementations.
• Act as a strategic thought partner to engineering managers, helping shape technical strategy and identify gaps, risks, and opportunities across ML platforms and systems.
• Collaborate with domain tech leads as a cross-domain advisor, aligning architectures, data pipelines, and modeling approaches across teams.
• Influence technical direction and best practices across the ML organization through design reviews, code reviews, and architectural guidance.
• Mentor senior engineers and help develop technical leadership across the team, without direct people management responsibility.

• BS/MS in Computer Science or an equivalent field with 8+ years of experience designing, building, and shipping production machine learning systems at scale
• At least two peer-reviewed publications in top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, KDD, ACL, CVPR, or equivalent), demonstrating strong ML fundamentals and technical depth
• Strong communication skills, with the ability to explain complex technical concepts clearly to both technical and non-technical audiences
• Professional work experience in Recommendation Systems or Causal Inference (Revenue or Growth)
• Strong hands-on engineering skills, with the ability to write, review, and debug production-quality code and ML pipelines
• Proven track record as a senior individual contributor (Staff or Principal level) of translating complex, ambiguous business problems into Machine Learning problems
• Deep understanding of end-to-end ML systems, including data pipelines, modeling, evaluation, deployment, and monitoring
• Experience of partnering closely with engineering managers and senior stakeholders to lead cross-team initiatives, shape technical direction and execution
• Hands-on experience with the following (or equivalent/similar) tools in production environments: Kubernetes, Triton Inference Server, Ray Serve, Airflow, Flink, or Spark (Databricks)

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:

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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