Top Tech Jobs & Startup Jobs in Singapore

Build and productionize machine learning models for semiconductor quality and manufacturing processes. Develop agentic AI, LLM, retrieval-augmented generation, tool-use, and memory capabilities for predictive quality workflows. Create maintainable analytical pipelines, optimize performance and token efficiency, and support testing, deployment, and monitoring. Communicate analytical findings to technical and non-technical stakeholders while applying AI tools responsibly.

Lead global teams for HBM customer integration and system engineering, drive technical interlocks, design reviews, escalations, and integration. Partner with design, product, manufacturing to translate customer requirements into product solutions. Own HBM architecture, packaging, manufacturing, customer enablement, scalable engagement models, governance, and executive communications. Resolve complex technical issues and influence product roadmaps. Develop and deploy AI systems to automate processes and solve technical challenges.

Architect and deploy autonomous Agentic AI systems for smart manufacturing. Responsibilities include developing multi-step AI agents, integrating LLMs with APIs and databases, building evaluation and profiling frameworks, implementing MCP and A2A interoperability, deploying agents through CI/CD to Kubernetes and cloud containers, securing agent workflows, optimizing inference performance, and mentoring engineers.

Intern will analyze supplier performance, capital equipment delivery, and procurement data; build Power BI and Excel dashboards; explore AI-enabled analytics and workflow automation; map procurement processes; and recommend improvements to efficiency, data quality, supplier visibility, and decision-making. The role involves cross-functional collaboration with procurement, engineering, operations, and suppliers, along with documentation and final presentation of project deliverables.

Designs, develops, tests, and validates AI-enabled solutions, workflow automation, analytics tools, and internal EHSS applications. Translates business processes into scalable technical solutions, builds prototypes and pilots, integrates APIs and enterprise platforms, and supports production deployment. Partners with EHSS, IT, data, AI, PMO, and technology teams to improve operational visibility, risk management, decision-making, governance, security, and long-term solution scalability.

Optimizes semiconductor fabrication processes, product yield, quality, reliability, cost, and productivity. Leads yield improvement, new product startups, data analysis, DOE, failure analysis, process qualifications, and cross-functional taskforces. Collaborates with wafer fab, process integration, equipment, materials, and test teams to resolve manufacturing issues and establish continuous improvement roadmaps. The role also uses AI-enabled tools responsibly to improve analysis and operational effectiveness.

Five-month internship analyzing semiconductor dry etch process and equipment data to identify variation, performance drift, productivity loss, and yield impacts. The intern will use statistical analysis, Python, SQL, JMP, Excel, dashboards, and AI-enabled methods to correlate manufacturing, metrology, and equipment data, then recommend process, hardware, recipe, or monitoring improvements.

Develop an AI-enabled dashboard for CMP equipment health and wafer breakage risk prediction. Analyze semiconductor manufacturing and equipment datasets, correlate maintenance and process indicators with breakage and yield excursions, build risk-scoring models, and create Power BI visualizations and automated engineering reports. The intern will collaborate with equipment engineers and gain experience in predictive analytics, reliability engineering, statistical analysis, and semiconductor manufacturing.

Senior Product Engineering role focused on functional and design validation of advanced NAND devices. Responsibilities include developing deterministic and pseudo-random Verilog test programs, automating validation with C, Python, and Perl, analyzing failures, identifying root causes, and driving cross-functional issue resolution. The role also applies AI/ML, data analytics, dashboards, and anomaly detection to improve test coverage, failure triage, debug productivity, and release readiness.

Lead data-driven process and test integration initiatives to improve high-value-bits yield (PG2+). Develop models and monitoring for tool performance, equipment prognostics, and test optimization. Prototype solutions with cross-functional teams, deploy best-known-methods to production, and integrate AI-assisted tools to drive continuous improvement and operational excellence.