Top Tech Jobs & Startup Jobs in Singapore

As Technical Lead, you will oversee application architecture, lead a senior engineering team, ensure reliability, and integrate AI capabilities into products, balancing speed and quality.

Lead execution of end-to-end ML systems: data pipelines, training workflows, evaluation, and inference. Translate research into production by fine-tuning large models (LoRA/QLoRA/SFT/DPO/distillation), designing scalable GPU inference, ensuring memory/latency optimization, building evaluation for performance/robustness/safety, and integrating ML into backend and client products while operating under real-world constraints.

The VP of Engineering leads application strategy for AI chat products, managing teams and ensuring quality and reliability across platforms.

Lead the AI product strategy, balancing technical feasibility with user needs, while collaborating closely with engineers for effective execution and quality control.

Build and maintain cross-platform Electron desktop apps focusing on reliability, performance, and real-time behavior. Implement desktop features (file access, notifications, auto-updates), integrate AI-powered backend services, optimize startup/memory, manage large local state, and ensure stability across macOS and Windows.

Lead production-grade ML systems: build and operate end-to-end ML pipelines, fine-tune large models (LoRA/QLoRA/SFT/DPO/distillation), design scalable inference, ensure GPU optimization, implement evaluation for performance/robustness/safety, and collaborate with product and application teams to ship reliable, low-latency AI features.

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 Software Engineer will develop and maintain cross-platform desktop applications using Electron, focusing on performance, reliability, and real-time updates for AI-powered features.

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.

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.