TL;DR

Senior Machine Learning Software Engineer, Backend (%hirify_global% Seoul): Bridging the gap between research and production by delivering machine learning models into real-world %hirify_global% features at scale with an accent on scalability and reliability in high-traffic environments. Focus on designing and building machine learning serving pipelines and maintaining backend services that enable ML models to be served, consumed, and monitored.

Location: Must be based in South Korea

Company

%hirify_global% revolutionized how people meet, growing from 1 match to one billion matches in just two years.

What you will do

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 %hirify_global%’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 %hirify_global%’s key business problems.
Ensure the scalability, reliability, and robustness of ML-driven systems operating in %hirify_global%’s high-traffic production environment.

Requirements

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.

Nice to have

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.

Culture & Benefits

Collaborative environment where people genuinely help each other.
Leadership team is always reachable no matter what level they are in.
Work hand-in-hand, building %hirify_global% for our members.
Take accountability and strive to make a positive impact in all aspects of our business, through ownership, innovation, and a commitment to excellence.
Cultivate a culture where it’s safe to take risks.
Intentional about building a workplace that reflects the rich diversity of our members.

Hiring 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)

Senior Machine Learning Software Engineer, Backend (Tinder Seoul)