Principal Machine Learning Engineer

About the role

A stealth AI startup, backed by one of Southeast Asia's leading technology companies and building its global founding team, is looking for a Principal Machine Learning Engineer. The company is developing an AI-native communication platform that integrates AI directly into conversations so users can plan, organize, compare, research and complete tasks in a single assistant. The engineer builds and deploys production-grade ML systems, working closely with research and application engineering teams.

Key responsibilities

• Build and own end-to-end ML pipelines covering data processing, model training, evaluation, inference and deployment.
• Fine-tune and adapt models using techniques such as LoRA, QLoRA, SFT, DPO and model distillation.
• Design and operate scalable inference systems while balancing latency, cost and reliability.
• Develop data pipelines for synthetic and real-world training datasets.
• Build evaluation frameworks for model performance, robustness, safety and bias with research teams.
• Optimize production deployments via GPU optimization, memory efficiency, latency reduction and scaling.
• Integrate ML systems into backend, mobile and desktop applications.

Required profile

• Strong background in deep learning and transformer-based architectures.
• Hands-on experience training, fine-tuning or deploying large-scale models in production.
• Ability to independently own end-to-end ML systems in fast-moving environments.
• Strong software engineering skills and strong problem-solving skills focused on rapid iteration.

Required skills

• PyTorch or JAX
• Distributed training and inference frameworks such as DeepSpeed, FSDP, Megatron, ZeRO or Ray
• GPU workload optimization, including quantization and mixed precision
• Preferred: LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer), RLHF pipelines, multimodal or diffusion models, and large-scale data frameworks such as Apache Arrow, Spark or Ray