Senior Machine Learning Software Engineer, Backend (Tinder Seoul)

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”™

Responsibilities

• Provide technical leadership within the ML software engineering team in Seoul — mentoring peers, setting engineering best practices, and driving projects from design to production delivery.
• Design and build machine learning serving pipelines, including daily and hourly batch jobs, to deliver model outputs reliably and efficiently to production systems.
• Develop and maintain backend services and distributed workers that enable ML models to be served, consumed, and monitored at scale across Tinder’s products.
• Collaborate closely with machine learning engineers to operationalize new models, ensuring smooth deployment, integration, and performance in production.
• Partner with ML engineers and product teams on LLM-related projects, applying large language models to deliver practical, measurable impact on Tinder’s key business problems.
• Take ownership of the software engineering components of the ML production stack, including orchestration, APIs, data pipelines, model versioning, and monitoring systems.
• Ensure the scalability, reliability, and robustness of ML-driven systems operating in Tinder’s high-traffic production environment.
• Work closely with cross-functional partners — including ML Engineers, ML Infrastructure Engineers, Backend Engineers, and CloudOps teams in the U.S. — to design and ship end-to-end ML solutions, requiring effective communication and collaboration in English.
• Deliver tangible business impact by integrating machine learning models into real-world Tinder features that improve user experience, trust, and engagement.

Qualifications

• 5+ years of experience in software engineering, with a focus on backend, machine learning, or data engineering.
• Strong foundation in CS fundamentals, including operating systems, computer architecture, data structures, and algorithms
• Experience in developing ML/AI-related services or a solid understanding of related engineering concepts
• English communication skills, with the ability to lead technical discussions and collaborate effectively with U.S.-based teams.
• Experience integrating and operating systems such as RDB, Redis, and Kafka
• Hands-on experience using big data batch and stream processing frameworks such as Spark or Flink.
• Hands-on experience using DataBricks for data pipeline or feature store 
• Hands-on experience deploying and managing applications in Kubernetes environments
• Experience operating infrastructure on AWS
• Proficiency in at least one programming language among Java, Kotlin, Golang, Python, or JavaScript (TypeScript), with the ability to quickly learn and adapt to other languages
• Self-motivated and proactive in taking ownership of tasks and driving them to completion

Preferred Qualifications

• Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve
• Experience with feature store systems and ML data pipelines supporting online/offline feature parity
• Practical experience building and optimizing data pipelines using modern orchestration frameworks (Airflow)
• Understanding of MLOps best practices including CI/CD for ML, model versioning, and automated evaluation or rollback strategies
• Experience with observability and monitoring tools for ML production systems (e.g., Prometheus, Grafana)
• Exposure to large language models (LLMs) and familiarity with deploying or fine-tuning them for applied use cases
• Experience working in cross-functional global teams, effectively collaborating across time zones and disciplines
• Strong understanding of machine learning algorithms and a genuine interest in applying them to production systems

Recruitment Process

• Employment Type: Full-time
• Recruitment Process: Document Screening > Coding Test > Hiring Manager/Recruiter Call > 1st Interview > 2nd Interview > 3rd Interview > Final Acceptance (*Most of the interview steps will be conducted in English)
• For document screening, only successful applicants will be notified individually.
• Application Documents: Detailed career-based English resume (PDF) in free format