Top Machine Learning Jobs in Singapore

As a Research Scientist-1, you will contribute to AI solutions, analyze data, write research papers, and collaborate with a diverse team.

Design, build, deploy, and optimize ML and AI solutions—including LLMs, RAG, multi-agent systems and function-calling—evaluate performance, test prompts, integrate models into production, and collaborate with cross-functional teams in a DevOps environment.

Design, build, deploy, and optimize scalable machine learning and AI solutions including LLMs, RAG systems and multi-agent/agentic workflows. Integrate models into production, evaluate performance, test prompts and few-shot examples, collaborate with cross-functional teams, and participate in code reviews, debugging and DevOps practices while adhering to regulatory and risk standards.

The intern will conduct research on driving behavior, collaborate across teams, utilize large datasets, and publish results related to Machine Learning.

Design, build, and operate an in-house AIOps/ML/LLM platform across cloud and on-prem Kubernetes. Implement production ML/LLM workflows (training, deployment, inference, monitoring, rollback). Troubleshoot across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers. Collaborate with cross-functional teams to translate client, security, and compliance requirements into platform designs.

The Principal Machine Learning Engineer will design and implement AI systems for game development, manage complex workflows, and optimize production models using Python and AWS.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

As a Senior Machine Learning Engineer - Search, you'll design and deploy advanced search and recommendation models, focusing on user intent and enhancing the GoFood and GoPay platforms.

Support embedded ML and biocomputational development for a wearable cardiometabolic platform. Tasks include signal processing, feature extraction, embedded firmware and sensor bring-up, dataset preparation/validation, dashboard visualization, documentation, and collaboration with clinical and research partners under close supervision.

The ML Engineer will build and scale large-scale data systems and pipelines, focusing on workload orchestration, dataset curation, and quality improvement techniques for machine learning models.

The AI and ML Solution Architect designs and implements AI/ML solutions, collaborates with teams, and improves product performance through advanced algorithms.

As an MLOps Engineer, design and maintain ML pipelines, automate deployments, collaborate with data scientists, and ensure model performance and governance.

Develop and optimize machine learning algorithms, train and evaluate AI models, preprocess data, collaborate on deployment, and drive research innovations in AI.

The role involves solving customer problems by building ML solutions, engaging with various stakeholders, and taking ownership of the outcome through deployment.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Responsible for developing and deploying production-grade machine learning systems, optimizing pipelines, and collaborating with engineering teams. Focus on deep learning and scalable inference systems.

The Senior Machine Learning Engineer will build and maintain crucial ML systems, ensure production reliability, debug issues, and mentor fellow engineers, focusing on end-to-end ownership and collaboration.

As Technical Lead, you will own the execution layer of ML systems, ensuring they are deployable, reliable, and efficient while collaborating closely with application engineering.

Lead the research direction for A1's AI system focusing on context representation, reasoning, planning, and orchestration. Decide on model architectures and evaluation frameworks while ensuring safety and alignment. Guide exploration of advanced techniques and shape product intelligence. Requires deep machine learning experience and a founder-like mentality.

As Technical Lead for Machine Learning, you will own and manage ML systems, ensuring they are production-ready while collaborating with engineering to integrate solutions.

As an AI/ML Software Engineer at Finite State, you'll develop AI-driven security features, integrate systems, and collaborate across teams, contributing to cybersecurity solutions for connected devices.

Design, fine-tune, evaluate, and deploy LLM/VLM models and multi-model agentic workflows. Build robust data pipelines, orchestration and benchmarking infrastructure, apply RL and knowledge representation, productionize AI systems, and mentor junior engineers while partnering with software and production teams.

Contribute to backend services and APIs in Python, support development and deployment of ML models, containerize applications, improve CI/CD and deployment workflows, debug and optimize system performance, and collaborate on system design while writing clean, documented code.

Design, fine-tune, evaluate, and deploy LLM/VLM models and multi-model agentic workflows. Build data pipelines, benchmarking and evaluation systems, and integrate AI solutions into production with software and production teams.

Conduct research on 3D/4D scene reconstruction and rendering, generative AI for video and 3D/4D generation, world models, and VLM/VLA systems. Build large-scale data pipelines, publish in top venues, contribute open-source code, mentor juniors, and report project progress.