Staff ML Engineer: Production-Grade AI Systems Lead

AI Chopping Block; Northern (KY); Hybrid; USD 170,000 - 210,000; Full time

Job summary

A1 in the United States is seeking a Technical Lead, Machine Learning to own the execution layer of our intelligence platform. You will translate research direction into reliable, production-grade ML systems across data pipelines, training, evaluation, inference, and deployment.

You will collaborate with engineering across backend, mobile, and desktop products, balance latency and cost, and push for robust, observable systems in real-world constraints. This is a hands-on, ownership-driven role.

Qualifications

• Experience shipping real ML systems used by users.
• Experience with large models and failure modes.
• Production-grade code with emphasis on correctness.

Responsibilities

• 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 with application engineering to integrate ML systems 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

Skills

ML system design

Python

PyTorch/JAX

GPU systems

Job description

A1 in the United States is seeking a Technical Lead, Machine Learning to own the execution layer of our intelligence platform. You will translate research direction into reliable, production-grade ML systems across data pipelines, training, evaluation, inference, and deployment.

You will collaborate with engineering across backend, mobile, and desktop products, balance latency and cost, and push for robust, observable systems in real-world constraints. This is a hands-on, ownership-driven role.