Staff Computer Vision Deployment Engineer (Production ML Infra)

Claryo; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Top-tier medical, dental, and vision coverage

401k with employer matching

Parental leave

Unlimited vacation

Job summary

Claryo is seeking a Staff Software Engineer with a focus on Computer Vision Deployment based in San Francisco. The successful candidate will develop robust infrastructures that power AI-driven warehouse intelligence. Responsibilities include creating and managing distributed cloud GPU infrastructures and building comprehensive computer vision pipelines. Candidates should have over 7 years of experience in software engineering and a proven record in deploying machine learning systems, particularly in production environments. The role is hybrid, requiring 3 days a week in the office.

Qualifications

• 7+ years of experience in software engineering, specifically in ML infrastructure.
• Experience deploying computer vision models in real-world environments.
• Strong programming skills in Python and software engineering practices.

Responsibilities

• Develop and maintain distributed cloud GPU infrastructure.
• Build end-to-end computer vision pipelines and integrate them into workflows.
• Deploy and optimize machine learning models using cloud platforms.

Skills

Machine Learning Infrastructure

Distributed Systems

Cloud Deployment

Python Programming

Computer Vision

Education

B.S. / M.S. in Computer Science, Robotics, or similar

Tools

PyTorch

TensorFlow

Kafka

gRPC

CUDA

Job description

Claryo is seeking a Staff Software Engineer with a focus on Computer Vision Deployment based in San Francisco. The successful candidate will develop robust infrastructures that power AI-driven warehouse intelligence. Responsibilities include creating and managing distributed cloud GPU infrastructures and building comprehensive computer vision pipelines. Candidates should have over 7 years of experience in software engineering and a proven record in deploying machine learning systems, particularly in production environments. The role is hybrid, requiring 3 days a week in the office.