Technical Lead, Machine Learning (AI)

TL;DR

Technical Lead, Machine Learning (AI/ML): Building and optimizing a proactive smart assistant to automate everyday tasks 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 scaling training pipelines.

Location: Remote (China)

Company

hirify.global is developing an AI-native proactive smart assistant designed to bring intelligence to conversations, errands, and workflows for billions of users.

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 while 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 usage, and latency reduction.

Requirements

• Proficiency in Python and PyTorch or JAX.
• Experience with GPU-based training and inference systems.
• Proven track record of shipping real ML systems to production, moving beyond prototypes and demos.
• Ability to write strong, production-grade code with a high standard for system correctness.
• Self-directed and pragmatic approach with full ownership of outcomes.
• Strong communication skills for collaboration within small, high-trust teams.

Culture & Benefits

• Work within a high-talent density, hands-on team of world-class engineers.
• Collaborative environment where decisions are made collectively and executed at rapid speed.
• Culture of balancing high-quality shipping with continuous learning.
• Opportunity to build a truly magical product with practical global benefits.

Hiring process

• Technical evaluation of applications followed by 3 to 4 interviews.
• Interviews conducted via virtual meetings or onsite.
• Transparent process with a commitment to prompt decisions.