Staff Machine Learning Engineer

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

As Technical Lead, Machine Learning, you own the execution layer of A1’s intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints.

What You'll Do

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
• Arcitect and operate scalable inference systems, balancing latency, cost, and reliability.
• Design and maintain data systems for high-quality synthetic and real-world training data.
• Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
• Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
• Work under real production constraints: latency, cost, reliability, and safety

Tech Stack

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

Ideal Experience

• You have built or shipped real ML systems used by people, not just demos.
• You are comfortable working with large models and understanding their failure modes.
• You write strong, production-grade code and care about system correctness.
• You are self-directed, pragmatic, and take full ownership of outcomes.
• You communicate clearly and collaborate well in small, high-trust teams.

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.