Remote Staff ML Engineer: Build & Scale AI Systems

Aimlroles; San Francisco (CA); Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Medical insurance

Dental

Vision

Savings Plan Options

PTO

Job summary

Aimlroles is seeking a Staff Machine Learning Engineer to own the execution layer of intelligent systems. You will translate research into reliable, scalable ML production workflows, including data pipelines, training, inference, and deployment across backend, mobile, and desktop environments.

You will fine-tune models with LoRA/QLoRA/SFT/DPO, design scalable inference, and partner with research leadership on evaluation pipelines for safety, bias, and performance.

Qualifications

• Experience building or shipping real ML systems.
• AI experience required.
• Experience with large models and their failure modes.
• Strong production-grade coding experience.
• Self-directed and ownership mindset.
• Clear communication and collaboration in small teams.

Responsibilities

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine-tune models using LoRA, QLoRA, SFT, DPO, and distillation.
• Architect scalable inference systems balancing latency, cost and reliability.
• Design and maintain data systems for high-quality training data.
• Implement evaluation pipelines covering performance, safety, and bias.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate with application engineering to integrate ML systems into products.
• Make pragmatic trade-offs and ship improvements quickly under real usage constraints.
• Work under production constraints: latency, cost, reliability, and safety.

Skills

Experience shipping ML systems

AI experience

Large models

Production-grade coding

Self-directed

Communication

Team collaboration

Tools

JAX

Python

PyTorch

Job description

Aimlroles is seeking a Staff Machine Learning Engineer to own the execution layer of intelligent systems. You will translate research into reliable, scalable ML production workflows, including data pipelines, training, inference, and deployment across backend, mobile, and desktop environments.

You will fine-tune models with LoRA/QLoRA/SFT/DPO, design scalable inference, and partner with research leadership on evaluation pipelines for safety, bias, and performance.