Principal Machine Learning Engineer
- Role
- AI / ML
- Experience
- Principal
- Employment
- Full-time
- Company size
- Startup
Open to SG only. Set where you work from to check your eligibility.
No BS summary
Principal ML engineer for production-grade AI/LLM systems in Singapore. Needs deep learning, transformer architectures, large-scale model training/fine-tuning/deployment, PyTorch or JAX, distributed training/inference, and GPU optimization.
Core skills
Required skills
Optional skills
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 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. Working closely with research and application engineering teams, this position will play a key role in delivering reliable, high-performance ML systems that operate effectively under real-world production constraints. 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. 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 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. Preferred Qualifications 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
What you'll do
- 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.
What they require
- 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.
- Preferred: Open-source contributions to machine learning or systems libraries.
- Preferred: Scientific computing, compiler technologies, or GPU kernel development.
- Preferred: Reinforcement Learning from Human Feedback (RLHF) pipelines, including PPO, DPO, or ORPO.
- Preferred: Training or deploying multimodal or diffusion models.
Dynamic and fast-growing multinational company in the food and beverage industry; a leading FMCG producer in Southeast Europe with over 20 brands available in over 40 markets worldwide.