onsite

Machine Learning Engineer - Search & Recommendations

This role involves designing and optimizing advanced machine learning models for search and recommendation systems across GoFood and GoPay. Responsibilities include building hybrid retrieval pipelines, developing deep ranking models, personalizing user experiences, and engineering recommendation algorithms using various ML techniques.

About the role

About the Role

As a Machine Learning Engineer specializing in Search & Recommendations, you will be instrumental in designing and optimizing advanced retrieval and ranking systems for GoFood and GoPay. Your work will directly impact user experience by improving the precision and recall of search results, personalizing recommendations, and enhancing the overall relevance of content presented to millions of users.

What You Will Do

Design and optimise hybrid lexical–semantic retrieval pipelines (e.g., BM25, dense vectors, HNSW/LSH, generative retrieval) to improve precision and recall across GoFood and GoPay surfaces.
Build high-quality embeddings and relevance signals that capture user intent, cuisine and dish semantics, geolocation, delivery constraints, price sensitivity, and promotions.
Develop multi-task deep ranking models that balance conversion, diversity, merchant quality, and long-term user retention, integrating real-time signals such as promotions, surge, and stock availability.
Build personalised ranking layers and user behaviour models leveraging historical orders, preferences, and contextual features.
Engineer recommendation algorithms using collaborative filtering, graph-based methods, and sequence models for retrieval expansion (e.g., Q2Q2I, Q2I2I, U2I), including for cold-start merchants and new dishes.
Advance embedding quality for multi-modal data (text, images, behavioural signals) and use LLMs to enhance structured knowledge (taxonomy tagging, dish attributes, dietary labels).
Incorporate structured metadata, taxonomy signals, and knowledge-graph features into retrieval and ranking pipelines to improve semantic understanding and consistency.

Skills

BM25dense vectorsHNSWLSHgenerative retrievalEmbeddingsdeep ranking modelscollaborative filteringgraph based methodssequence modelsQ2Q2IQ2I2IU2ILlmstaxonomy tagging

DepartmentEngineering

LocationJakarta, Indonesia

Experience3+ years

Tenurefull-time

LevelMid-Level

Posted June 1, 2026

About the role

About the Role

What You Will Do

Design and optimise hybrid lexical–semantic retrieval pipelines (e.g., BM25, dense vectors, HNSW/LSH, generative retrieval) to improve precision and recall across GoFood and GoPay surfaces.
Build high-quality embeddings and relevance signals that capture user intent, cuisine and dish semantics, geolocation, delivery constraints, price sensitivity, and promotions.
Develop multi-task deep ranking models that balance conversion, diversity, merchant quality, and long-term user retention, integrating real-time signals such as promotions, surge, and stock availability.
Build personalised ranking layers and user behaviour models leveraging historical orders, preferences, and contextual features.
Engineer recommendation algorithms using collaborative filtering, graph-based methods, and sequence models for retrieval expansion (e.g., Q2Q2I, Q2I2I, U2I), including for cold-start merchants and new dishes.
Advance embedding quality for multi-modal data (text, images, behavioural signals) and use LLMs to enhance structured knowledge (taxonomy tagging, dish attributes, dietary labels).
Incorporate structured metadata, taxonomy signals, and knowledge-graph features into retrieval and ranking pipelines to improve semantic understanding and consistency.