– Legal Entity: Hyperconnect
– Brand: Tinder
– Team: Tinder ML Seoul Team

Team Overview
The Tinder Machine Learning (ML) team plays a vital role across nearly all core areas of the product, including Recommendations, Trust & Safety, Profile, Growth, and Revenue. Our mission is to use machine learning to improve user experiences, build trust, and drive business growth throughout the Tinder platform.

The ML team is structured into three specialized groups:

– Machine Learning Engineers who focus on developing models and advancing algorithms.
– Machine Learning Infrastructure Engineers who create scalable platforms and tools for training, serving, and managing features.
– Machine Learning Software Engineers (this role), who connect research with real-world application by deploying ML models into Tinder’s large-scale production systems.

This team is essential in transitioning models from experimentation to deployment, ensuring they are reliable, efficient, and impactful. Many of our models are already integrated into key Tinder features, influencing millions of user interactions daily.

The team works closely with ML engineers and infrastructure teams in both the U.S. and Seoul to build scalable and dependable systems for high-traffic environments. This role sits at the intersection of machine learning and software engineering, ensuring that ML models are effectively incorporated into Tinder’s products.

Responsibilities

– Provide technical leadership within the ML software engineering team in Seoul, mentoring team members, establishing best practices, and managing projects from design to deployment.
– Design and implement ML model serving pipelines, including batch jobs, to deliver model outputs reliably to production systems.
– Build and maintain backend services and distributed systems that support scalable ML model serving and monitoring across Tinder.
– Collaborate with ML engineers to deploy and integrate new models smoothly into production.
– Work with ML and product teams on projects involving large language models (LLMs) to solve key business challenges.
– Own the software components of the ML production stack, including orchestration, APIs, data pipelines, model versioning, and monitoring.
– Ensure ML systems are scalable, reliable, and robust in Tinder’s high-traffic production environment.
– Collaborate with cross-functional teams — including ML Engineers, ML Infrastructure Engineers, Backend Engineers, and CloudOps in the U.S. — to deliver complete ML solutions, requiring strong communication skills in English.
– Drive measurable business impact by integrating ML models into Tinder features that enhance user experience, trust, and engagement.

Qualifications

– 5+ years of experience in software engineering, particularly in backend, ML, or data engineering.
– Solid understanding of computer science fundamentals, including operating systems, architecture, data structures, and algorithms.
– Experience developing ML/AI services or strong knowledge of related engineering concepts.
– Strong English communication skills for leading technical discussions and collaborating with U.S.-based teams.
– Experience with systems like RDB, Redis, and Kafka.
– Hands-on experience with big data batch and stream processing tools such as Spark or Flink.
– Experience using DataBricks for data pipelines or feature stores.
– Experience deploying and managing applications in Kubernetes environments.
– Experience managing infrastructure on AWS.
– Proficiency in at least one programming language such as Java, Kotlin, Golang, Python, or JavaScript (TypeScript), with the ability to learn others quickly.
– Self-driven and proactive in taking ownership of tasks and delivering results.

Preferred Qualifications

– Familiarity with ML model serving tools like TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve.
– Experience with feature store systems and maintaining consistency between online and offline features.
– Practical experience building and optimizing data pipelines using orchestration frameworks like Airflow.
– Knowledge of MLOps best practices, including CI/CD for ML, model versioning, and automated evaluation or rollback.
– Experience with observability tools for ML systems, such as Prometheus and Grafana.
– Exposure to large language models (LLMs) and experience deploying or fine-tuning them for real-world use.
– Experience working in global, cross-functional teams across time zones.
– Strong understanding of ML algorithms and a passion for applying them in production environments.

Recruitment Process

– Employment Type: Full-time
– Hiring Process: Application Review > Coding Test > Call with Hiring Manager/Recruiter > 1st Interview > 2nd Interview > 3rd Interview > Final Offer (Most interviews will be conducted in English)
– Only shortlisted candidates will be contacted after the document screening.
– Required Documents: Detailed English resume (PDF) in any format, focused on professional experience.

Note: If any false information is found in your application or if you are legally ineligible for employment, your offer may be revoked. Additional screening or documentation may be requested as needed.

Veterans and eligible individuals will be given preference in accordance with applicable laws. Please inform us during the application process and provide supporting documents if selected.

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