As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing.
We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more.
Our Values
Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down.
Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today.
Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate.
Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same.
Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us.
Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters.
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.
ML at Tinder is organized into three groups with distinct roles:
Machine Learning Engineers who focus on modeling and algorithmic innovation (this role)
Machine Learning Infrastructure Engineers 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 by delivering machine learning models into real-world product experiences at scale
About the Role
We are looking for a Machine Learning Engineer II to help build and ship machine learning systems that improve product experience and drive measurable business impact. This role is ideal for an engineer with a strong foundation in machine learning and software engineering who is excited to work on real-world problems, partner cross-functionally, and grow quickly in a high-impact environment.
This is an individual contributor role focused on modeling and algorithmic innovation. You will work closely with product, engineering, data, and platform partners to translate product opportunities into machine learning solutions, run experiments, and help bring models from development into production. The team’s work directly translates into measurable business outcomes, and many of its models are embedded in core Tinder user flows at scale.
This is a hybrid role and requires in-office collaboration three times per week in Palo Alto, California.
In this role, you will:
Translate product and business problems into clear machine learning problems with measurable success criteria
Build, train, evaluate, and improve production machine learning models
Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production
Design and analyze offline evaluations and online experiments to understand model impact
Contribute to feature engineering, data preparation, training pipelines, and model monitoring
Write clean, maintainable, production-quality code and participate in design and code reviews
Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical partners
You'll need:
BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field
2+ years of industry experience in machine learning, software engineering, data science, or a related field
Strong foundation in computer science fundamentals, including data structures, algorithms, and software design
Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated
Proficiency in Python and at least one additional programming language such as Java, Kotlin, Go, Scala, or a similar language
Strong understanding of machine learning fundamentals, including model training, evaluation, and experimentation
Strong communication skills and the ability to collaborate effectively across functions
Self-motivated, proactive, and comfortable taking ownership of well-scoped problems
Nice to have:
Experience with recommendation systems or casual inference
Familiarity with big data or stream processing frameworks such as Spark or Flink
Familiarity with cloud platforms such as AWS and containerized environments such as Kubernetes
Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve
Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow
Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation
Exposure to observability and monitoring for ML systems
Exposure to LLM-related use cases or applied generative AI projects
Skills Required
- BS or MS in Computer Science, Machine Learning, or related field
- 2+ years of industry experience in machine learning or software engineering
- Proficiency in Python and at least one additional programming language
- Strong understanding of machine learning fundamentals and software design
Match Group Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Match Group and has not been reviewed or approved by Match Group.
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Retirement Support — Employer matching on retirement savings and access to an employee stock purchase program are highlighted as standout elements. Feedback suggests this creates strong long-term financial support as part of total rewards.
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Parental & Family Support — Fully paid parental leave, fertility and family-forming support, and childcare resources are emphasized across materials. Feedback suggests these benefits meaningfully support different family needs.
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Leave & Time Off Breadth — Generous PTO, numerous paid holidays, wellness and volunteer time, and other special leave options are consistently described. Feedback suggests the breadth of time-off options helps sustain work-life balance.
Match Group Insights
What We Do
Match Group is home to some of the world’s most popular dating and social discovery apps, including Tinder, Hinge, Match, and more. Match Group’s mission is to spark meaningful connections for every single person worldwide. Our diverse portfolio of apps enables connections across a diverse range of ages, genders, backgrounds, and dating goals. Our services are available in over 40 languages to users all over the world.
Why Work With Us
Match Group is united by a mission to help people find meaningful connections and fight loneliness. We’re not just building products. We’re creating friendships, marriages, and families around the world. Our culture is fueled by purpose, creativity, and a passion for what we can build together.
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