Lowongan Kerja Senior Machine Learning Di GoTo

Gabung WA Lowongan Kerja KLIK DISINI

Bersama ini kami sampaikan informasi Lowongan Kerja Senior Machine Learning Di GoTo sebagai berikut:

Posisi: Senior Machine Learning

Persyaratan

• Master’s degree or higher in Computer Science, Machine Learning, NLP, CV, or a related field; strong programming skills in Python, C++, or Java.
• Hands-on experience building large-scale ranking or recommendation systems in consumer products (ecommerce, food delivery, rideshare, ads, streaming, social).
• Familiarity with LLMs and/or LLVMs. Experience integrating them into search or recommendation pipelines is a strong plus.
• Demonstrated ability to innovate with new algorithms or tools and drive measurable impact, especially making use of Large language Models (LLMs) and Large Language and Vision models (LLVMs) in search or recommendation modeling.
• Strong product intuition and ability to reason from user behavior data and traffic patterns.
• Good communication skills in English, both written and verbal.
• Self-motivated, curious, and excited by the opportunity to build high-impact systems quickly.

Deskripsi Pekerjaan

• 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.

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