Machine Learning Engineer (Contents Understanding) at Match Group

Seoul, South Korea

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Social NetworkingIndustries

Requirements

Broad understanding of AI/ML domains and deep knowledge in at least one specific domain
Strong interest in productizing AI technologies
Proven capability to solve problems using AI, including project management skills
Strong Python development skills, including experience with open-source frameworks like TensorFlow, PyTorch, CatBoost, JAX
Strong ownership to lead and complete projects from A to Z
Excellent communication skills for collaborating with diverse stakeholders across teams
Engineering expertise to understand ML system software architecture and plan features
Ability to identify statistical characteristics and patterns in data and apply them to AI problem-solving
Experience planning A/B tests, defining target KPI metrics, and conducting SQL-based data analysis
Fluent communication in Korean (degree and nationality irrelevant)

Responsibilities

Research and improve state-of-the-art models while considering real-world constraints for engineering decisions
Identify product issues, reframe them as AI problems, and drive solutions end-to-end
Lead the full problem-solving lifecycle: problem definition, stakeholder alignment, goal setting, SOTA model derivation, scheduling, performance analysis, and future strategy
Collaborate closely with backend/frontend/DevOps/ML engineers, data analysts, PMs, and other teams
Define priorities based on user needs and business impact to drive long-term product growth via AI
Extract actionable insights from unstructured data (video, images, audio, natural language) to support content moderation, Trust & Safety, and global standards
Design lightweight models for mobile/web with low latency, low power, and high accuracy
Manage label quality in noisy/imbalanced data using active learning, core-set selection, semi-/self-supervised methods
Optimize multi-task/multi-label classification within parameter constraints and integrate multi-modal data (text, image, video)
Apply domain adaptation and meta-learning for cross-domain challenges and service scalability
Implement fairness, privacy-preserving learning to meet international AI standards
Develop streaming models for real-time anomaly detection (e.g., spam, fake accounts) using user logs and content analysis
Leverage LLMs to innovate ML production processes
Utilize on-premise GPU clusters (A100/H100) and cloud data pipelines for model development and deployment

Skills

Key technologies and capabilities for this role

Machine LearningDeep LearningComputer VisionNatural Language ProcessingAudio ProcessingMulti-Modal LearningActive LearningSelf-Supervised LearningSemi-Supervised LearningMulti-Task LearningMulti-Label ClassificationDomain AdaptationMeta-LearningModel OptimizationLow-Latency InferenceData PipelinesCloud Services

Questions & Answers

Common questions about this position

What salary information is provided for this Machine Learning Engineer position?

This information is not specified in the job description.

Is this Machine Learning Engineer role remote or office-based?

This information is not specified in the job description.

What are the key required skills for this Machine Learning Engineer role?

Required skills include understanding of AI/ML domains with deep knowledge in at least one area, Python development with frameworks like Tensorflow, PyTorch, CatBoost, JAX, strong project ownership for A-Z management, communication for cross-functional collaboration, ML systems engineering, data pattern analysis, and A/B testing with SQL.

What is the company culture like at Hyperconnect AI Lab?

The AI Lab fosters a collaborative environment where ML Engineers work closely with backend/frontend/DevOps engineers, data analysts, and PMs to solve product problems using ML, emphasizing research-to-production blending, strong ownership of projects from A-Z, and contributions to real product growth in social discovery.

What makes a strong candidate for this Machine Learning Engineer position?

Strong candidates demonstrate AI/ML domain expertise, Python proficiency with ML frameworks, project ownership from A-Z, cross-functional communication, production deployment experience improving key metrics, and preferably top-tier publications or English fluency.

Match Group

Provides online dating and social discovery

About Match Group

Match Group leverages the Swipe feature® and social discovery, facilitating deeper connections through its portfolio of online dating brands, with a global presence and availability in over 40 languages.

Dallas, TexasHeadquarters

1986Year Founded

$400MTotal Funding

IPOCompany Stage

Consumer Software, Social ImpactIndustries

1,001-5,000Employees