Team Introduction The Tinder ML team drives impact across nearly every core domain of the product — from Recommendations and Trust & Safety to Profile, 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 team at Tinder is organized into three groups with different roles: - Machine Learning Engineers who focus on modeling and algorithmic innovation. - Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training, serving, and feature management. - Machine Learning Software Engineers (this role) who bridge the gap between research and production — delivering machine learning models into real-world Tinder features at scale. This team plays a critical role in taking models from experimentation to production, ensuring they’re robust, performant, and impactful. Our work directly translates into measurable business outcomes — many of our models are already embedded in core Tinder user flows, influencing millions of daily interactions in real time. This team collaborates closely with ML engineers, ML infra engineers teams across the U.S. and Seoul to design and develop systems optimized for scalability and reliability in high-traffic environments. This role plays a role at the intersection of ML and software engineering — ensuring that machine learning models are effectively integrated into real-world products.

• 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.

• 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

• 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

• 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