TL;DR

Senior Machine Learning Software Engineer, Backend (AI): Building and optimizing machine learning serving pipelines and backend services to integrate ML models into %hirify_global%'s high-traffic production environment with an accent on scalability, reliability, and real-time model delivery. Focus on operationalizing new models, applying large language models for business impact, and collaborating across global teams to design end-to-end ML solutions.

Location: Onsite in Seoul, South Korea

Company

%hirify_global% is a global dating app that revolutionized how people meet, serving approximately 50 million users monthly across 190 countries.

What you will do

Provide technical leadership, mentoring peers, 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.
Develop and maintain backend services and distributed workers that enable ML models to be served, consumed, and monitored at scale.
Collaborate closely with machine learning engineers and product teams on LLM-related projects to deliver practical business impact.
Take ownership of the software engineering components of the ML production stack, ensuring scalability, reliability, and robustness.
Work closely with cross-functional partners in the U.S. and Seoul to design and ship end-to-end ML solutions.

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.
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, and DataBricks for data pipelines or feature stores.
Experience deploying and managing applications in Kubernetes environments on AWS, and proficiency in at least one programming language among Java, Kotlin, Golang, Python, or JavaScript (TypeScript).

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 MLOps best practices, including CI/CD for ML, model versioning, and automated evaluation.
Practical experience building and optimizing data pipelines using modern orchestration frameworks (Airflow).
Exposure to large language models (LLMs) and familiarity with deploying or fine-tuning them for applied use cases.

Culture & Benefits

Collaborative environment where people genuinely help each other and leadership is always reachable.
Emphasis on accountability, innovation, and a commitment to excellence.
Culture that cultivates continuous learning, safely takes risks, and learns from mistakes.
Problem-solving mindset, focusing on how to best move forward when faced with obstacles.
Commitment to building a workplace that reflects the rich diversity of members and leverages different perspectives.

Hiring process

Recruitment Process: Document Screening > Coding Test > Hiring Manager/Recruiter Call > 1st Interview > 2nd Interview > 3rd Interview > Final Acceptance.
Most interview steps will be conducted in English.
Application requires a detailed career-based English resume (PDF).