About the role
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
Proven experience building and deploying production-grade machine learning systems used by real users.
Strong expertise working with large language models and understanding model behavior, limitations, and failure modes.
Experience developing scalable ML infrastructure, training pipelines, and inference systems.
Strong software engineering skills with the ability to write maintainable, production-quality code.
Experience balancing real-world production constraints, including latency, reliability, scalability, cost, and safety.
Strong ownership mindset with the ability to independently drive technical initiatives from design through deployment.
Excellent communication and collaboration skills, with experience working in cross-functional, high-performing engineering teams.
Preferred Technical Skills
Experience with the following technologies is preferred:
Python
PyTorch and/or JAX
GPU-based model training and inference systems
Originally posted on Himalayas