Remote ML Technical Lead — Production AI Systems

Harper Adams University; United States; Remote; USD 190,000 - 225,000; Full time

Benefits offered by this job

Medical insurance

Dental

Vision

Savings plan

PTO

Job summary

Harper Adams University is seeking a Machine Learning Technical Lead for a fully remote role. You will own the execution layer of intelligence, translating research into production-grade ML systems that are trainable, scalable, observable, and performant under real-world constraints.

You will lead end-to-end ML pipelines, fine-tune models with LoRA/QLoRA and related methods, design scalable inference systems, and collaborate with engineering to integrate ML into backend, mobile, and desktop

Qualifications

• Experience building or shipping real ML systems used by people.
• AI experience required.
• Experience with large models and failure modes.
• Production-grade code writing experience.
• Self-directed and ownership mindset.
• 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 training data quality.
• Implement evaluation pipelines covering performance, robustness, safety, and bias.
• Own production deployment including GPU optimization and scaling policies.
• Collaborate with application engineering to integrate ML into backend and apps.
• Ship improvements quickly learning from real usage.
• Work under real production constraints: latency, cost, reliability, safety.

Skills

AI experience

Production-grade code

Self-directed

Clear communication

Tools

JAX

Python

PyTorch

LoRA

QLoRA

SFT

DPO

Distillation

Job description

Harper Adams University is seeking a Machine Learning Technical Lead for a fully remote role. You will own the execution layer of intelligence, translating research into production-grade ML systems that are trainable, scalable, observable, and performant under real-world constraints.

You will lead end-to-end ML pipelines, fine-tune models with LoRA/QLoRA and related methods, design scalable inference systems, and collaborate with engineering to integrate ML into backend, mobile, and desktop