Machine Learning Engineer

About the Role

A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, 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.

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.

• 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

• 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.

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

• 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.