ML Systems Lead — Production‑Grade AI

AI Chopping Block; Northern (KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

A1 is seeking a Technical Lead, Machine Learning to own the execution layer of its intelligence platform. You will translate research direction into reliable, scalable ML systems for production environments, balancing latency and cost while ensuring safety and reliability.

You will collaborate with research and engineering to deploy end-to-end pipelines, optimize GPUs, and ship practical improvements in fast-moving sprints.

Qualifications

• Experience shipping production ML systems.
• Comfort with large models and failure modes.
• Strong, production-grade coding skills.
• Self-directed, pragmatic with ownership.
• Clear communication in small teams.

Responsibilities

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine-tune and adapt models using 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

Python

PyTorch

JAX

LoRA/QLoRA/SFT

Distillation

Tools

GPU-based training

Job description

A1 is seeking a Technical Lead, Machine Learning to own the execution layer of its intelligence platform. You will translate research direction into reliable, scalable ML systems for production environments, balancing latency and cost while ensuring safety and reliability.

You will collaborate with research and engineering to deploy end-to-end pipelines, optimize GPUs, and ship practical improvements in fast-moving sprints.