Principal Machine Learning Engineer

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 be responsible for turning research direction into working, production-grade ML systems.

This role owns the execution layer of A1’s intelligence – training pipelines, inference systems, evaluation tooling, and deployment. Focus Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, 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 Requirements Strong background in deep learning and transformer-based architectures. Hands-on experience training, fine-tuning, or deploying large-scale ML models in production. Proficiency wi