About the Role
A1 is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.
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.
What You'll Do
- 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
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.