VP of Research, Machine Learning (AI)

TL;DR

VP of Research, Machine Learning (AI): Defining the research and intelligence direction for a proactive AI system with an accent on context representation, reasoning, and planning. Focus on designing model architectures, building evaluation frameworks, and implementing frontier techniques like MoE and multi-agent orchestration.

Location: Remote (Must be based in the United Kingdom)

Company

hirify.global is developing A1, a proactive AI system designed to maintain context across conversations and plan complex actions to provide practical benefits globally.

What you will do

• Set and evolve the research direction for A1’s core intelligence, covering memory, reasoning, and orchestration.
• Determine whether to design custom model architectures or leverage frontier open-source and commercial models.
• Define evaluation frameworks to measure real-world usefulness, robustness, and long-term behavior.
• Lead the strategy for alignment, safety, and guardrails as primary product concerns.
• Guide exploration of RAG, mixture-of-experts, distillation, and multimodal systems.
• Collaborate with product and application engineering to shape early product intelligence.

Requirements

• Deep experience building and evolving production-grade machine learning systems.
• Strong technical judgment regarding model behavior, failure modes, and long-horizon trade-offs.
• Builder’s mindset focused on real-world system functionality rather than academic publishing.
• Ability to make high-impact decisions independently with incomplete information.
• High ownership mentality, operating with a founder's perspective.
• Location: Must be based in the United Kingdom

Culture & Benefits

• High talent density environment working in small, world-class teams.
• Culture of collective decision-making and rapid shipping speed.
• Hands-on approach balancing high-quality output with continuous learning.
• Opportunity to build a "magical" product with potential global impact for billions of users.

Hiring process

• Initial evaluation of applications by the technical team.
• 3 to 4 interviews conducted via virtual meetings and/or onsite.
• Transparent and efficient process with a prompt final decision.