VP of Research, Machine Learning

About A1

There are over 5 billion users using basic applications today such as email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organizing, and workflows, with minimal prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily with over ~90%* reduced time.

Role

You will own the research and intelligence direction of this system. Your role is to 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.

Tech Stack

• Python
• PyTorch / JAX
• GPU-based training and inference system

How We Work

The best products today in the world were built by small, world-class teams. We are a high-talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high-quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in the hands of our users a truly magical product.

Interview Process

If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.