Job Title

PairsPairs2,50020122013202570Pairs2015Match Group ''Pairs'' is an online dating app used by more than 25 million people in Japan. Since launch, we have received success stories from over 700,000 people. Pairs is the first-in-market to offer 24/7 in-house customer service, including text and image monitoring, so our users'' safety and security are always ensured. We created Pairs to help singles discover new and interesting ways to find a life partner. In the United States, more than 1 in 3 couples met their partner online. In Japan more than 70 per cent of singles claim to have no partner. As the number 1 online dating service in Japan, we''re working hard every day to help singles find their true love. Since the release of Pairs in October 2012, we have helped to create opportunities for many users to find partnership and marriage. Pairs offers the opportunity to find an ideal partner that matches one''s own values through various search features and a well-developed MyTag. Everyday, we receive many reports of successful matches from across Japan and from overseas. It is our hope that more and more opportunities for wonderful relationships will be created, which inspires us to improve our service every day. embeddings / vector search candidate generation / retrieval As a Machine Learning Engineer, you will work on projects where recommendation, search, NLP, and image processing are core to the system. You will focus on improving recommendation and search functionalities - particularly through embeddings, vector search, and retrieval (including candidate generation) - to enable product teams to effectively leverage these outputs and continuously drive product KPI improvements. UX Partner closely with product teams from the early planning stage, supporting Product Managers in defining project objectives, success metrics (KPIs) and expected outcomes; Conduct handson analysis and proof of concept (PoC), build prototypes, and drive alignment across stakeholders by improving understanding of the solution and its impact; Design and implement ML-driven systems while balancing tradeoffs across model performance (e.g., accuracy, latency impacting UX), cost and development lead time; Actively document and share knowledge, insights, and decisions using internal collaboration tools (e.g., documentation, meeting notes). IT etc. Work on machine learningdriven feature development using data from one of the largest online dating services in Japan, collaborating with product teams from the planning stage, and gaining knowledge and technical skills through delivering optimal solutions; At Eureka, engineers own technology decisions and are empowered to adopt the latest technologies as needed; Work with highly service-oriented team members and develop coordination, communication, and problem-solving skills through discussions on best practices; Gain handson experience in driving a truly datadriven organization that balances both qualitative and quantitative perspectives; Experience the logical thinking and highspeed execution typical of a global IT company; improve English skills through communication with global stakeholders and international team members across Match Group(English learning support is provided); Contribute to social impact through product development (e.g., addressing declining birth rates, delayed marriage, and enabling freedom of choice in life). / Python, Go BigQuery, Amazon Aurora MySQL, DynamoDB, Redis, Elasticsearch Google Cloud Vertex AI, Google Cloud Dataflow, Cloud Pub/Sub, Fluentd, Fluent Bit, dbt, Dagster etc BITableau, Superset Terraform GitHub, Slack, Jira, Confluence Google Workspace Google Sheets / Google Slides AIChatGPT Enterprise / Codex, Claude Code, Cursor, Gemini (Google Workspace), AI LLM / Langfuse(self-hosted) AWS/Google Cloud Programming Languages: Python, Go Databases: BigQuery, Amazon Aurora MySQL, DynamoDB, Redis, Elasticsearch Data Processing: Google Cloud Vertex AI, Dataflow, Pub/Sub, Fluentd, Fluent Bit, dbt, Dagster, etc. BI Tools: Tableau, Superset Infrastructure as Code: Terraform Development & Collaboration: GitHub, Slack, JIRA, Confluence Google Workspace: Google Sheets, Google Slides AI Tools: ChatGPT Enterprise, Codex, Claude Code, Cursor, Gemini (Google Workspace), as well as internally developed AI tools and plugins LLM Observability / Monitoring: Langfuse (self-hosted) Infrastructure: AWS and Google Cloud services WebApp Web Sr. Backend engineer 2 General knowledge, skills, and experience as a software engineer, including: Web application development Understanding of overall web application architecture; Backend development experience (design and implementation); experience at a Senior Backend Engineer level is a plus; Minimum of 2 years of fulltime professional experience. : EC / embeddings / vector search candidate generation / retrieval Elasticsearch / Solr two-sided market Experience working on and continuously improving software systems where search or recommendation is a core component: E.g., product recommendation in ecommerce, job/candidate matching, improving recommendations using nearestneighbor retrieval over user embeddings/vector, optimizing candidate generation/retrieval, and improving search results using Elasticsearch/Solr; Managed services are acceptable for these components; Experience in a twosided marketplace domain is a plus. / Strong communication skills with both business and engineering stakeholders: Ability to define project outcomes in collaboration with Product Managers, analysts, and, when needed, leadership, and translate them into machine learning requirements; Ability to structure complex requirements, engage relevant stakeholders, and drive alignment. Japanese proficiency at business level or above For nonnative Japanese speakers, experience working in a Japanesespeaking environment is preferred; Given that Japan is the primary market, a willingness to deepen understanding of Japanese language and culture is preferred. Match Group English proficiency at business level or above: Ability to communicate with Englishspeaking stakeholders within Match Group and create formal documentation in English, or the ability to ramp up to that level. 1. 2. 1. UX 2. A/B embeddings / vector search candidate generation / retrieval A/BKPI/KGI NumPy, Pandas, SciPy, Matplotlib, scikitlearn, TensorFlow, PyTorch 3. SQL Python/Ruby/Java/Scala/Go / RDB 4. ML Kubeflow / TFX ML Kubernetes SageMaker, Vertex AI managed ML services ML Langfuse LLM observability / tracing / monitoring 5. 1 6. BigQuery / DynamoDB feature store Application 7. SRESLO, #J-18808-Ljbffr