TL;DR

Machine Learning Software Engineer (AI/Backend): Designing and building scalable machine learning serving pipelines and backend services to integrate models into %hirify_global%'s product ecosystem, with an accent on operationalizing LLMs, ensuring system reliability, and driving measurable business impact. Focus on developing robust ML production stacks, optimizing for high-traffic environments, and fostering cross-functional collaboration.

Location: South Korea (Seoul)

Company

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

What you will do

Design and build ML serving pipelines, batch jobs, and backend services for model delivery.
Operationalize new ML models, ensuring smooth deployment and performance in production.
Collaborate with ML engineers and product teams on LLM-related projects.
Own software engineering components of the ML production stack, including orchestration and monitoring.
Ensure scalability, reliability, and robustness of ML-driven systems in high-traffic environments.
Integrate machine learning models into %hirify_global% features to improve user experience and engagement.

Requirements

2+ years of experience in software engineering, with a focus on backend, machine learning, or data engineering.
Strong foundation in CS fundamentals, data structures, and algorithms.
Experience developing ML/AI-related services or understanding related concepts.
English communication skills for collaborating with U.S.-based teams.
Experience integrating and operating systems like RDB, Redis, or Kafka.
Proficiency in Java, Kotlin, Golang, Python, or JavaScript (TypeScript).

Nice to have

Practical experience with big data/stream processing frameworks (Spark, Flink) or Databricks.
Familiarity with Kubernetes and cloud environments (AWS).
Experience with ML model serving frameworks (TensorFlow Serving, TorchServe).
Understanding of MLOps best practices (CI/CD for ML, model versioning).
Exposure to large language models (LLMs) and fine-tuning.

Culture & Benefits

Collaborative environment with emphasis on teamwork across functions and time zones.
Focus on accountability, innovation, and continuous improvement.
Culture that supports risk-taking, feedback, and learning from mistakes.
Problem-solving mindset, focusing on agility and overcoming hurdles.
Commitment to diversity and inclusion in the workplace.

Hiring process

Document Screening, Coding Test, Hiring Manager/Recruiter Call.
Multiple interview rounds (1st, 2nd, 3rd Interview).
Final Acceptance. Most interview steps in English.
Detailed career-based English resume (PDF) required.

Machine Learning Software Engineer, Backend (AI)