Staff Machine Learning Engineer (AI)

TL;DR

Staff Machine Learning Engineer (AI): Own the execution layer of proactive AI chat app intelligence, translating research into reliable, scalable production-grade ML systems with an accent on data pipelines, model fine-tuning, inference architecture, and deployment. Focus on architecting scalable inference systems, implementing evaluation pipelines for robustness and safety, and optimizing for latency, cost, and reliability under real-world constraints.

Location: Remote (Singapore)

Company

hirify.global is building a proactive AI chat app for everyday users, focusing on high reliability for long-running workflows, persistent context, multi-step reasoning, external tool interactions, and real-world task completion.

What you will do

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine-tune and adapt models using LoRA, QLoRA, SFT, DPO, and distillation.
• Architect scalable inference systems balancing latency, cost, and reliability.
• Design data systems for synthetic and real-world training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias.
• Handle production deployment with GPU optimization, memory efficiency, and scaling.
• Collaborate with application engineering to integrate ML into backend, mobile, and desktop products.

Requirements

• Built or shipped real ML systems used by people, not just demos.
• Comfortable with large models and their failure modes.
• Strong production-grade code with focus on system correctness.
• Self-directed, pragmatic, full ownership of outcomes.
• Clear communication and collaboration in small, high-trust teams.

Culture & Benefits

• Small, world-class, high-talent-density teams.
• Collective decision-making, rapid speed, balance between quality and learning.
• Hands-on execution with structure, judgment, and independence.

Hiring process

• 3-4 interviews via virtual meetings and/or onsite, evaluated by technical team.
• Prompt decisions with transparency and efficiency.