Principal Machine Learning Engineer

Stack

Responsibilities

• ▹Build and own end-to-end machine learning pipelines covering data processing, model training, evaluation, inference, and deployment
• ▹Fine-tune and adapt models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation
• ▹Design and operate scalable inference systems while balancing latency, cost, and reliability
• ▹Develop and maintain data pipelines for both synthetic and real-world training datasets
• ▹Build evaluation frameworks to assess model performance, robustness, safety, and bias in collaboration with research teams
• ▹Optimize production deployments through GPU optimization, memory efficiency, latency reduction, and scaling strategies
• ▹Collaborate with application engineering teams to integrate machine learning systems into backend, mobile, and desktop applications
• ▹Continuously improve ML systems through rapid iteration and real-world performance monitoring

Requirements

• ▹Strong background in deep learning and transformer-based architectures
• ▹Hands-on experience training, fine-tuning, or deploying large-scale machine learning models in production
• ▹Proficiency with modern machine learning frameworks such as PyTorch or JAX
• ▹Experience with distributed training and inference frameworks, including DeepSpeed, FSDP, Megatron, ZeRO, or Ray
• ▹Strong software engineering skills with experience building robust, maintainable, production-grade systems
• ▹Experience optimizing GPU workloads, including memory efficiency, quantization, and mixed precision
• ▹Ability to independently own end-to-end machine learning systems in fast-moving environments

Nice to have

• ▹LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer
• ▹Open-source contributions to machine learning or systems libraries
• ▹Scientific computing, compiler technologies, or GPU kernel development
• ▹Reinforcement Learning from Human Feedback pipelines (PPO, DPO, ORPO)
• ▹Training or deploying multimodal or diffusion models
• ▹Large-scale data processing frameworks such as Apache Arrow, Spark, or Ray

Soft skills

About the company

Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies, currently building its global founding team. It is developing an AI-native communication platform that simplifies everyday tasks by integrating AI directly into conversations, so users no longer need to switch between multiple applications.