Machine Learning Software Engineer (Match Group AI)
Match Group is hiring a Machine Learning Software Engineer (Match Group AI) in Seoul, South Korea. Level rates it ; you can apply on Match Group.
Level's read on this job
- Level rates this software engineer role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include Claude, vLLM and Ray.
- Level classifies it as a mid-level role.
- Level lists 59 other open roles at Match Group.
AI in this role
Match Group AI Team Introduction
- Detailed article: Introduction to Match Group AI Team (written in Korean)
Match Group AI (MG AI) is the central tech organization that drives innovation across Match Group’s global portfolio, including Tinder, Hinge, Azar, Pairs, Match, BLK, etc. Our mission is to solve the most complex challenges in online dating (e.g., Recommendation, Trust & Safety, Profile Enhancement) by bridging cutting-edge AI research with excellent software engineering.
Unlike brand-specific teams (e.g., Tinder, HYPERCONNECT AI), MG AI team offers the unique opportunity to impact the entire Match Group ecosystem. You won't just build for one app; we aim to develop scalable AI solutions that power Tinder, Hinge, and beyond, defining the technological gold standard for the global dating industry.
Working as a MLSE at MG AI
- On-device AI Face Verification Pipeline Optimization
- 10 Python Performance Optimization Tips for High-Performance ML Backends
While ML Engineers focus on modeling, Machine Learning Software Engineers (MLSEs) at MG AI team focus on the critical bridge between research and production. We ensure that state-of-the-art models (including LLMs and Multimodal systems) are integrated into high-traffic environments, serving millions of users in real-time.
We also upload a selection of interesting problems solved by our team's engineers to the Hyperconnect Tech blog (written in Korean).
What You’ll Do
- Build ML Services: Design and develop backend services and distributed systems that enable the seamless consumption, scaling, and monitoring of ML models.
- Build ML Pipelines: Design and develop ML serving pipelines (real-time and batch) to deliver model outputs with low latency and high reliability.
- Improve Recommendation System: Improve the AI-powered personalized recommendation system to help users find better matches on Match Group’s dating apps (serve better retrieval/ranking/utility models, add better features, etc).
- Co-Engineering with Brands: Engage in deep technical collaboration with engineering counterparts (Tinder, Hinge, Match, Azar, Pairs, etc.) across various global offices, including Seoul, Palo Alto, LA, Vancouver, Dallas, and Tokyo.
What We're Looking For
- 2+ years of experience in software engineering, with a focus on Backend, ML Engineering, or Data Engineering.
- Strong understanding of CS Fundamentals (data structures, algorithms, operating systems) and distributed system design.
- Proficiency in at least one modern programming language (e.g., Python, Go, Java, Kotlin, C#) and a "polyglot mindset" to adapt to new stacks quickly.
- A strong interest in how ML models are built and a passion for solving the engineering challenges of deploying them in the real world.
- Proficiency in leveraging AI-powered tools (e.g., Claude Code, Codex, Cursor) to accelerate productivity.
- Professional working proficiency in English. (Able to conduct business meetings and participate in complex discussions without requiring assistance.)
- Fluent in Korean (Sophisticated professional interactions with native-level precision): Essential for cross-functional collaboration within the Seoul office.
Nice to Have
- Experience with the full ML lifecycle, from model training to production deployment.
- Experience in building AI-based recommendation system.
- Experience in developing scalable backend servers (handling millions of users).
- Experience in developing and serving ML-driven services using frameworks such as vLLM, Triton, Ray Serve, or Seldon.
- Experience with big data or stream processing frameworks (e.g., Spark, Flink, Kafka) for building robust ML data pipelines.
- Experience in collaborating with cross-functional teams and diverse organizations.
- Fluent in English (Sophisticated professional interactions with native-level precision and an understanding of cultural nuances.)
Hiring Process
- Work type: Full-time
- Legal Entity: Hyperconnect / Brand: Match Group AI
- Recruitment Process: Resume Review > Live Coding Interview > Technical Interview > Technical Deep-dive & Cultural Alignment Interview > Offer
- (* Please note that part of the interview process may be conducted in English)
- For the document screening stage, only successful candidates will be individually notified of the results.
- Application documents: A detailed, career-based English resume (PDF) in free format
- This position is open to candidates seeking to transfer as Alternative Military Service Researchers (전문연구요원). For those under this program, service management will be conducted in accordance with the relevant military service exemption laws.
How we rate this
Machine Learning Software Engineer (Match Group AI) at Match Group rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in software engineer jobs at this level
Pay
- Typical software engineer job
- $168k
This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- Walk me through how you've used Claude in your day-to-day work.
- What are the limits of vLLM that you've run into, and how did you work around them?
- What's a project where you used Ray hands-on?
- Walk me through how you've used Cursor in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: AI Research, Claude, vLLM, Ray, and Cursor. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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