VP of Research – Machine Learning

Role Overview

• Own the research and intelligence direction of this system.
• Define how AI reasons, evaluates, and improves in a product used with high frequency.
• Set and evolve the research direction for A1’s core intelligence, including context representation, memory, reasoning, planning, and orchestration.
• Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models.
• Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior – not benchmark vanity.
• Own alignment, safety, and guardrail strategy as first-class product concerns.
• Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal systems.
• Shape early product intelligence direction in close partnership with product and application engineering.
• Set the technical bar for research rigor, judgment, and taste across the organization.

Requirements

• Deep experience building or evolving real machine learning systems used in production.
• Strong technical judgment around model behavior, failure modes, and long-horizon trade-offs.
• A builder’s mindset: you care about systems that work in the real world, not just ideas.
• Comfortable making irreversible or high-impact decisions with incomplete information.
• Obsession with evaluation, correctness, and how systems behave over time.
• High ownership mentality — you operate as a founder, not a manager. If you are looking to focus primarily on publishing, incremental benchmarks, or managing a large research organization, this role will not be a fit.