AI Engineer (LLM)

TL;DR

AI Engineer (LLM): Building and owning end-to-end ML pipelines for a proactive AI smart assistant with an accent on training, inference, and production-grade deployment. Focus on fine-tuning transformer-based models, optimizing GPU performance, and ensuring system reliability for real-world task completion.

Location: Must be based in or able to work from Seoul, Korea (Hybrid)

Company

hirify.global is a high-talent density product company building a proactive AI smart assistant designed to bring intelligence to everyday user workflows.

What you will do

• Build and own end-to-end ML pipelines covering data, training, evaluation, and deployment.
• Fine-tune models using state-of-the-art methods like LoRA, QLoRA, SFT, and DPO.
• Architect and operate scalable inference systems while balancing latency, cost, and reliability.
• Design data systems for high-quality synthetic and real-world training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias.
• Collaborate with application engineering to integrate ML systems into product interfaces.

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 modern ML frameworks like PyTorch or JAX.
• Experience with distributed training and inference frameworks such as DeepSpeed, FSDP, or Ray.
• Strong software engineering fundamentals for writing robust, production-grade systems.
• Experience with GPU optimization, memory efficiency, and mixed precision.

Nice to have

• Experience with LLM inference frameworks like vLLM or TensorRT-LLM.
• Background in scientific computing, compilers, or GPU kernels.
• Experience with RLHF pipelines (PPO, DPO, ORPO).
• Experience training or deploying multimodal or diffusion models.
• Contributions to open-source ML or systems libraries.

Culture & Benefits

• Work in a high-talent density, hands-on team environment.
• Collaborative decision-making process with rapid execution cycles.
• Focus on shipping high-quality, magical products for global users.
• Opportunity to solve complex challenges in latency, reliability, and safety.

Hiring process

• Technical evaluation by the team.
• 3 to 4 interviews conducted via virtual meetings or onsite.
• Transparent and efficient decision-making process.