Technical Lead, Machine Learning

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.