Machine Learning Engineer

Join Tinder's ML team as a Machine Learning Engineer II, where you'll drive impact across core product domains such as Recommendations, Trust & Safety, Profile, Chat, Growth, and Revenue optimization. In this individual contributor role, you'll focus on modeling and algorithmic innovation, translating product opportunities into machine learning solutions, and helping bring models from development into production. You'll work closely with product, engineering, data, and platform partners, and your work will directly translate into measurable business outcomes.

• Translate product and business problems into clear machine learning problems with measurable success criteria.
• Build, train, evaluate, and improve production machine learning models, and partner with software engineers to deploy models.
• Design and analyze offline evaluations and online experiments to understand model impact, and contribute to feature engineering.
• 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
• Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow
• Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve
• Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation
• Strong communication skills and the ability to collaborate effectively across functions
• 1+ year of industry experience in machine learning, software engineering, data science, or a related field
• Self-motivated, proactive, and comfortable taking ownership of well-scoped problems
• BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field
• Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated
• 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
• 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 foundation in computer science fundamentals, including data structures, algorithms, and software design
• Exposure to LLM-related use cases or applied generative AI projects
• Exposure to observability and monitoring for ML systems