28 days ago
Singapore, SingaporeStaff+
Responsibilities
- Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.
- Fine-tune and adapt models using state-of-the-art methods such as LoRA and QLoRA.
- Architect and operate scalable inference systems, balancing latency, cost, and reliability.
- Design and maintain data systems for high-quality training data.
- Implement evaluation pipelines covering performance, robustness, safety, and bias.
- Own production deployment, including GPU optimization and scaling policies.
- Collaborate with application engineering to integrate ML systems into products.
- Make pragmatic trade-offs and ship improvements quickly.
Requirements
- Strong background in deep learning and transformer-based architectures.
- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX).
- Experience with distributed training and inference frameworks (e.g. DeepSpeed, Ray).
- Strong software engineering fundamentals for robust, maintainable systems.
- Experience with GPU optimization, including memory efficiency and quantization.
- Comfort owning ambiguous, zero-to-one ML systems end-to-end.
- A bias toward shipping, learning fast, and improving systems through iteration.
Tech Stack
Apache SparkPyTorch
Categories
AI & MLData Engineering