VP of Research, Machine Learning
Job description
About A1
There are over 5 billion users using basic applications today such 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, organising 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 enjoyable 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.
What You'll Do
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.
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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.
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 hands of our users a truly magical product
Interview process
If there appears to be a fit, we'll reach 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.
Originally posted on Himalayas