Founding Backend Engineer for AI Infrastructure

Fluency Digital, Inc.; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Fluency Digital, Inc. is a seed-stage AI startup located in San Francisco, California, that is building specialized intelligence tools for various professions. The company is looking for an engineer who excels in solving ambiguous infrastructure problems, constructing backend systems that power AI models, and balancing experimentation with reliability.

The ideal candidate will have 3 to 7 years of experience in backend or machine learning engineering, be highly proficient in Python, and have extensive experience with AWS or GCP environments. A strong understanding of scalable architecture design and optimizing machine learning models in production is essential.

Qualifications

• 3 to 7 years of backend or machine learning engineering experience with production systems.
• Deep proficiency in Python and extensive hands-on work with AWS or GCP environments.
• Proven ability to design scalable architectures from scratch in an early-stage setting.
• Strong understanding of deploying and optimizing machine learning models in production.
• Must be based in the United States.

Responsibilities

• Construct the backend systems that power domain-specific AI models.
• Balance rapid experimentation with reliability for professional deployment.
• Shape how specialized agents operate on cloud infrastructure.

Skills

Python

AWS

GCP

Job description

Fluency Digital, Inc. is a seed-stage AI startup located in San Francisco, California, that is building specialized intelligence tools for various professions. The company is looking for an engineer who excels in solving ambiguous infrastructure problems, constructing backend systems that power AI models, and balancing experimentation with reliability.

The ideal candidate will have 3 to 7 years of experience in backend or machine learning engineering, be highly proficient in Python, and have extensive experience with AWS or GCP environments. A strong understanding of scalable architecture design and optimizing machine learning models in production is essential.