onsite

Machine Learning Engineer

The Machine Learning Engineer will be responsible for designing and optimizing retrieval pipelines and building high-quality embeddings and relevance signals for GoFood and GoPay. This role involves developing multi-task deep ranking models, personalized ranking layers, and recommendation algorithms, as well as advancing embedding quality for multi-modal data and leveraging LLMs.

About the role

Responsibilities

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 modelsQ2Q2IQ2I2IU2Imulti modal datatextimagesbehavioural signalsLlmstaxonomy taggingdish attributesdietary labelsstructured metadata

DepartmentEngineering

LocationJakarta, Indonesia

Experience3+ years

Tenurefull-time

LevelMid-Level

Posted June 1, 2026

About the role

Responsibilities

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.