Research jobs in Singapore

The specific roles and responsibilities are subject to change and evolve and may include but not be limited to any of the following areas:

• Design, build, and deploy advanced analytics and machine learning models aligned to SAS strategic priorities, including decision-support systems, workflow automation, recommender systems, and advanced data visualizations that improve teaching, learning, and operations.
• Lead development of AI copilots, agentic workflows, and RAG-based tools that help staff and educators interrogate information, improve productivity, and make evidence-informed decisions.
• Build evaluation, observability, and monitoring pipelines to ensure AI models remain accurate, safe, fair, and aligned with SAS policy and ethical guidelines.

• Create and maintain a highly automated ETL pipeline ecosystem that integrates all major school systems (SIS/LMS, assessment platforms, HR/Finance, operations systems, and third-party tools).
• Architect and optimize the SAS data lake with strong data models, lineage, and governance to support real‑time analytics and future AI integrations.
• Partner with Data Governance to ensure data quality, documentation, and long‑term maintainability of the school’s data infrastructure.

• Embed ethical, secure, and privacy-preserving AI practices across all engineering work, including: data minimization, anonymization, PII protection, guardrails, and secure access controls.
• Implement robust evaluation frameworks for safety, bias detection, and responsible use of generative and predictive systems.
• Ensure all systems and integrations meet SAS policies, regulatory requirements, and industry best practices for responsible innovation.

• Work closely with the Educational Technology team to translate instructional problems into technical solutions that support assessment, personalization, student progress insights, and instructional planning.
• Collaborate with IT Operations to ensure infrastructure, deployment, and security models align with scalable cloud-first practices.
• Engage with external vendors, research partners, and EdTech organisations to benchmark SAS against emerging trends and inform future design.

• Participate in professional learning opportunities, staying abreast of trends in AI and emerging technologies and their application in education.
• Complete other duties as assigned by the Technology Leadership Team.

• Bachelor’s Degree in Computer Science, Mathematics, Software Engineering, Computer Engineering, or related field.
• Proven track record delivering production‑grade AI systems, including generative AI, RAG, or agentic workflows, from discovery through deployment and ongoing monitoring.
• Strong English communication skills with the ability to translate technical complexity into clear, actionable insights for non‑technical partners.

• Programming & Frameworks: Python; JavaScript/TypeScript; modern AI/ML frameworks (e.g., LangChain/LangGraph); API development (REST/GraphQL); microservices and serverless patterns.
• AI/ML & Data Engineering: LLM tooling (prompting, RAG, safety/guardrails), vector stores (e.g., FAISS/Pinecone), data warehouses (e.g., BigQuery), dashboarding (Looker Studio), feature stores, and automated data pipelines.
• Cloud & Infrastructure: Cloud platforms (GCP/AWS/Azure), containerization (Docker/Kubernetes), CI/CD pipelines, observability, and secure, cost‑efficient deployments.
• Systems Integration: Experience integrating enterprise systems, ideally SIS/LMS, assessment platforms, authentication/SSO using secure APIs, webhooks, and data contracts.

• Advanced degree in a relevant field
• Prior work in EdTech, K–12, higher education, or social‑impact contexts, especially building tools that improve instructional time, assessment, student support, or school operations.
• Background working with UX/UI designers to create educator‑friendly products with high adoption and usability.
• Familiarity with data governance, AI policy, digital citizenship, or responsible innovation frameworks.
• Knowledge of multi‑modal AI, simulation environments, agentic orchestration, or frontier model evaluation practices.