Principal Machine Learning Engineer

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.

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 with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.

- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).

- Strong software engineering fundamentals – you write robust, maintainable, production-grade systems.

- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.

- Comfort owning ambiguous, ze...