Technical Lead, Machine Learning (AI)

TL;DR

Technical Lead, Machine Learning (AI): Building a proactive AI smart assistant for everyday users with an accent on high reliability for long-running workflows and multi-step reasoning. Focus on translating research into production-grade ML systems, optimizing GPU inference, and implementing state-of-the-art fine-tuning methods.

Location: Hybrid (Seoul, Korea)

Company

hirify.global is developing a high-intelligence proactive AI smart assistant designed to handle complex conversations, errands, and workflows globally.

What you will do

• Own end-to-end ML system execution, including data pipelines, training workflows, evaluation systems, and deployment.
• Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
• Architect and operate scalable inference systems, balancing latency, cost, and reliability.
• Design and maintain data systems for high-quality synthetic and real-world training data.
• Implement evaluation pipelines covering performance, robustness, safety, and bias.
• Optimize production deployment, focusing on GPU efficiency, memory management, and latency reduction.

Requirements

• Proven experience building and shipping production ML systems used by actual users.
• Proficiency in Python, PyTorch, and/or JAX.
• Hands-on experience with GPU-based training and inference systems.
• Ability to write strong, production-grade code with a high standard for system correctness.
• Experience working with large models and a deep understanding of their failure modes.
• Location: Must be based in or able to work hybrid in Seoul, Korea

Culture & Benefits

• High talent density environment with a small, world-class, and hands-on team.
• Culture of collective decision-making and rapid shipping speed.
• Focus on high-quality work and continuous learning.
• Opportunity to build a truly magical product with practical benefits for billions of users.
• Transparency and efficiency in internal processes.

Hiring process

• Technical evaluation by team members.
• 3 to 4 interviews conducted via virtual meetings and/or onsite.
• Prompt decision-making process.