Research Scientist-Model Efficiency (AI)

Location: Singapore, SG or Austin, US

Company

hirify.global is a technology company focused on Bitcoin mining, AI cloud services, computing infrastructure, and high-performance computing datacenters.

What you will do

• Optimize large language models for faster and more cost-efficient production inference.
• Implement and adapt methods including quantization, sparsity, pruning, speculative decoding, MTP, and attention or KV-cache optimization.
• Develop new model-efficiency techniques when existing approaches do not meet quality or performance requirements.
• Adapt optimizations to internal models and hardware platforms.
• Build rigorous evaluation practices using task-level metrics, controlled comparisons, and honest baselines.
• Collaborate on production serving and AI infrastructure improvements.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or a related field.
• Hands-on experience with LLM inference, model optimization, or machine learning systems.
• Strong programming skills in Python and deep familiarity with PyTorch.
• Implementation-level expertise in at least one model-efficiency area, such as quantization, sparsity and pruning, speculative decoding, MTP, or serving-time attention and KV-cache methods.
• Strong understanding of transformer internals, accuracy loss, and model behavior, together with rigorous evaluation practice.
• Experience with production inference, inference engines such as vLLM, SGLang, or TensorRT-LLM, and technologies such as C++, CUDA, or Triton is advantageous or highly preferred depending on the area.

Nice to have

• Publications at top-tier systems or machine learning venues.
• Substantial open-source contributions.
• Experience taking efficiency methods into production serving.

Culture & Benefits

• Inclusive environment that values authenticity and diverse perspectives.
• Startup spirit within a fast-growing technology company.
• Opportunities to contribute directly to new AI infrastructure and digital asset projects.
• Autonomy, personal accountability, and opportunities for rapid growth and learning.
• Training, mentoring, welfare benefits, and professional development opportunities.