Tinder
Technology
SoftwareEngineerII,MachineLearning
Neural analysis suggests this role is
optimal for Mid candidates.
“Software Engineer II, Machine Learning at Tinder. Skills: Machine Learning, Software Engineering, Algorithmic innovation. Translate problems into machine learning problems. Build production machine learning models”
What You'll Achieve.
Enhance user experiences; Foster trust; Accelerate business growth; Drive measurable business impact; Improve product experience; Achieve measurable success criteria
Industry & Context.
Solve complex problems
What They're Looking For.
Must Have
BS or MS in Computer Science, 1+ year of industry experience, Foundation in computer science fundamentals, Experience building ML or AI systems, Proficiency in Python, Proficiency in at least one additional programming language, Understanding of machine learning fundamentals
Nice to Have
Experience with recommendation systems, Experience with casual inference, Familiarity with big data frameworks, Familiarity with stream processing frameworks, Familiarity with cloud platforms, Familiarity with containerized environments, Familiarity with ML model serving frameworks, Experience with feature stores, Experience with ML data pipelines, Experience with orchestration frameworks, Understanding of MLOps practices, Exposure to observability for ML systems, Exposure to monitoring for ML systems, Exposure to LLM-related use cases, Exposure to applied generative AI projects
What You'll Do.
Translate problems into machine learning problems
Build production machine learning models
Train production machine learning models
Evaluate production machine learning models
Improve production machine learning models
Deploy models with software engineers
Deploy models with ML infrastructure engineers
Improve reliability in production
Improve scalability in production
Improve performance in production
Design offline evaluations
Analyze offline evaluations
Design online experiments
Analyze online experiments
Contribute to feature engineering
Contribute to data preparation
Contribute to training pipelines
Contribute to model monitoring
Write production-quality code
Participate in design reviews
Participate in code reviews
Communicate technical findings
Communicate trade-offs
Communicate recommendations
How You'll Work.
Team & Collaboration
Partner cross-functionally; Work with product partners; Work with engineering partners; Work with data partners; Work with platform partners; Collaborate across functions
Communication Scope
Technical findings; Trade-offs; Recommendations
Full Job Description
## Description Our Mission 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 Team or Role: 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
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