Technical Lead, Machine Learning

About A1: There are over 5 billion users using basic applications today such as email, notes, and 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 behaviour. Our objective is to help users complete tasks daily enjoyably 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.
• Architect 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.

Outcomes:

• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.
• Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
• Iterations on models and systems are measurable, safe, and improve user experience over time.

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.