About the Company

Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team.

The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant.

Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive.

About the Role

Our client is seeking a Technical Lead, Machine Learning to lead the execution of its AI platform by translating research into scalable, production-ready machine learning systems. This role sits at the intersection of research, infrastructure, and product, with responsibility for ensuring models are trainable, deployable, observable, and optimized for real-world performance.

Working closely with research, engineering, and product teams, this position will drive the development of robust ML infrastructure while balancing performance, reliability, latency, and cost.

Key Responsibilities

• Lead the end-to-end execution of machine learning systems, including data pipelines, training workflows, evaluation frameworks, inference architecture, and production deployment.
• Fine-tune and optimize models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation.
• Design, build, and operate scalable inference systems with a focus on latency, cost efficiency, and reliability.
• Develop and maintain data pipelines for both synthetic and real-world training datasets.
• Build evaluation frameworks to measure model performance, robustness, safety, and bias in collaboration with research teams.
• Optimize production deployments through GPU utilization, memory efficiency, inference optimization, and scaling strategies.
• Partner closely with application engineering teams to integrate machine learning systems into backend, desktop, and mobile products.
• Continuously improve production systems through rapid iteration, monitoring, and data-driven optimization.

Requirements