Deep Learning Research Specialist

2 weeks ago


Singapore beBeeResearch Full time $120,000 - $180,000
About Our Research Scientist Role

We're seeking a highly skilled Research Scientist who embodies both scientific curiosity and engineering rigor. This role is ideal for someone passionate about developing reliable, scalable experimentation methodologies, large-model training, and tight integration with MLOps and systems engineering.

Key Responsibilities:
  • Develop high-performance training pipelines for deep learning research that leverage scalability and efficiency
  • Tune and debug large model training runs across clusters and GPU environments to optimize performance
  • Collaborate closely with systems engineers to ensure infrastructure supports rapid experimentation and deployment
  • Work together with other research scientists to evolve architecture and optimization techniques, driving innovation in the field
  • Establish best practices for model reproducibility, logging, and deployment readiness to ensure seamless transitions
Requirements:
  • Proven experience in training ML models at scale using frameworks like PyTorch or JAX, with a strong focus on scalability and efficiency
  • Proficiency in Python and familiarity with deep learning optimization internals, including knowledge of GPU compute fundamentals and training-performance bottlenecks
  • A track record of working on real-world datasets with practical ML applications, demonstrating a commitment to clean, maintainable, and reproducible research code
  • Familiarity with SLURM, cluster scheduling, or multi-GPU orchestration to optimize resource utilization
Bonus Points:
  • Experience with TorchScript, ONNX, or custom inference runtimes to enhance model deployment and optimization
  • Contributions to open-source ML tooling, such as PyTorch, Lightning, or Hugging Face, showcasing expertise in collaborative development
  • Hands-on experience with transformer models, diffusion models, or large-scale vision/NLP tasks to stay ahead of industry trends
  • Prior collaboration with MLOps or systems engineering teams to demonstrate cross-functional expertise


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