Principal Machine Learning Engineer

📋 Job Overview

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.

🏢 About avomind

avomind is a stealth AI startup focused on creating an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations.

🎯 The Role

Our client is seeking a Principal Machine Learning Engineer to build and deploy production-grade machine learning systems that power its AI platform. This role focuses on translating research into scalable solutions by developing robust training pipelines, inference systems, evaluation frameworks, and deployment infrastructure.

✅ Key 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 while balancing production constraints such as latency, cost, reliability, and safety.

📌 Required Qualifications

• 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 technologies such as 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.
• Strong problem-solving skills with a focus on rapid iteration and continuous improvement.

⭐ Desirable Experience

• Experience with one or more of the following is preferred:
• 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 (RLHF) pipelines, including PPO, DPO, or ORPO
• Training or deploying multimodal or diffusion models
• Large-scale data processing frameworks such as Apache Arrow, Spark, or Ray

🎁 Benefits

Competitive salary package.