Machine Learning Engineer (AI/ML)

OZION TECH PTE. LTD.; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

OZION TECH PTE. LTD. in Singapore is seeking a skilled Machine Learning Engineer to join their AI team. You will be responsible for building cutting-edge recommendation systems that enhance user experiences across gaming platforms. The role requires proficiency in AI technologies and strong collaboration with different teams.

The ideal candidate holds a Bachelor’s degree in Computer Science or related fields and has experience in recommendation systems and AI technologies like TensorFlow and PyTorch.

Qualifications

• Bachelor’s degree or above in Computer Science, AI, Data Science, or Mathematics.
• Strong foundational knowledge of recommendation systems.
• Proficiency in mainstream AI/ML frameworks and tools.

Responsibilities

• Apply AI and ML technologies to optimize recommendation models and systems.
• Design user lifecycle value-oriented recommendation strategies.
• Collaborate with Product, Data, and Operations teams.

Skills

Recommendation systems

AI/ML frameworks (TensorFlow, PyTorch)

User behavior analysis

Image content recommendation

Education

Bachelor’s degree in Computer Science or related field

Tools

Hadoop

Spark

Flint

Job description

About the Role

We are seeking a skilled Machine Learning Engineer to join our AI team and build cutting-edge recommendation systems for gaming content. You will leverage state-of-the-art AI technologies to personalize user experiences and drive engagement across our gaming platforms.

Responsibilities

Apply state-of-the-art AI and machine learning technologies to design, build, and optimize core recommendation models and systems, including but not limited to collaborative filtering, deep learning, and graph neural networks (GNNs), to continuously improve system performance and relevance.

• Develop AI-driven content matching and user modeling strategies tailored to gaming scenarios (e.g., immersive content interactions), aligning recommendations with user preferences and behavioral patterns.
• Design and implement user lifecycle value (LTV)-oriented recommendation strategies, optimizing traffic allocation and content distribution logic to maximize long-term user value.
• Design high-efficiency content recommendation strategies aligned with user acquisition, retention, and engagement goals.
• Collaborate closely with Product, Data, and Operations teams to translate business requirements into practical AI/ML solutions that enhance overall user experience.
• Support in-game image content distribution systems; improve content relevance and matching quality through advanced algorithm optimization.
• Track cutting-edge advancements in AI and recommender systems (e.g., large-scale retrieval, reinforcement learning, multimodal learning), and drive the real-world implementation of technical innovations in gaming scenarios.

Requirements

• Bachelor’s degree or above in Computer Science, Artificial Intelligence, Data Science, Mathematics, or a related quantitative field.
• Strong foundational knowledge of recommendation systems, including retrieval, ranking, and cold-start mechanisms.
• Familiarity with computer vision algorithms and models; hands‑on experience in multimodal modeling is a strong plus.
• Proficiency in mainstream AI/ML frameworks and tools (TensorFlow, PyTorch, Spark MLlib).
• Solid experience with large-scale distributed data processing systems (e.g., Hadoop, Spark, Flink).
• Strong user behavior analysis and predictive modeling capabilities; deep understanding of user engagement metrics and LTV optimization.
• Experience in image content recommendation, short‑form video recommendation systems, or gaming content distribution is highly preferred.
• Excellent cross‑functional collaboration and communication skills, with demonstrated ability to align technical objectives with business goals.
• Exceptional learning agility and adaptability, with the ability to rapidly master emerging technologies and apply them to production environments.

Nice to Have

• Experience with reinforcement learning in recommendation contexts.
• Publications in top-tier ML conferences (NeurIPS, ICML, KDD, etc.).
• Familiarity with MLOps practices and model serving infrastructure.