Staff Machine Learning Engineer (AI)

TL;DR

Staff Machine Learning Engineer (AI): Building proactive AI systems for everyday task completion with an accent on persistent context and high reliability for long-running workflows. Focus on architecting scalable training and inference pipelines, optimizing model performance for production, and integrating complex ML systems into real-world applications.

Location: Must be based in Palo Alto, California (Hybrid role)

Company

hirify.global is building A1, a proactive AI chat assistant designed to bring intelligence to workflows, errands, and daily organization through reliable, long-running agentic tasks.

What you will do

• Own end-to-end ML execution, including data pipelines, training workflows, and deployment architecture.
• Fine-tune models using modern methods like LoRA, QLoRA, SFT, and DPO.
• Architect and operate scalable inference systems while balancing latency, cost, and reliability.
• Design systems for high-quality synthetic and real-world training data.
• Build evaluation pipelines to monitor performance, safety, and robustness.
• Collaborate with product and engineering teams to integrate ML models into cross-platform products.

Requirements

• Must be able to work from the Palo Alto office (Hybrid).
• Proven experience shipping production-grade ML systems, not just research demos.
• Strong proficiency in Python, PyTorch, or JAX.
• Deep understanding of model architecture, training loops, and failure modes.
• Experience with GPU-based training and inference optimization.
• Ability to take full ownership of technical outcomes in a high-trust, small-team environment.

Culture & Benefits

• High talent density environment with a focus on ownership and collective decision-making.
• Rapid development cycles balancing speed with high-quality engineering.
• Opportunity to build products with a global scale and impact.
• Collaborative team culture built on transparency and efficiency.

Hiring process

• Technical evaluation of application.
• 3 to 4 rounds of interviews (virtual or onsite).