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Senior AI Serving Engineer, Backend

Sciforium· Engineering
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San Francisco, CA$190k - $250kFull-timePosted 8mo ago

About the role

Senior backend role focused on building Sciforium’s model serving platform for multimodal AI models, working across C++, Python, runtime execution, and distributed infrastructure to create a fast, reliable engine for real-time AI applications.

Responsibilities

  • Build the model serving platform, including API, Control Plane, Billing, Monitoring, and distributed inference features.
  • Collaborate with ML researchers to integrate new multimodal models into production workflows.
  • Write reliable, maintainable code with strong testing and documentation practices.
  • Provide operational support for keeping our production services highly performant, available and reliable
  • Help troubleshoot complex issues across runtime, service, and GPU layers, working closely with other engineers.

Qualifications

  • Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
  • 3+ years of software engineering experience, with a focus on infrastructure or machine learning systems.
  • Strong proficiency in C++/Python/Go/Rust
  • Experience with Kubernetes, Containerization
  • Experience in building large scale ML/MLOps infrastructure
  • Strong collaboration and communication skills, with the ability to work effectively across engineering and ML teams.
  • Comfortable working from the office and contributing to a fast-moving, high-ownership team culture.

Skills

Required:C++PythonKubernetes
Nice to have:GoRust

Benefits

  • Medical, dental, and vision insurance
  • 401k plan
  • Daily lunch, snacks, and beverages
  • Flexible time off
  • Competitive salary and equity
Last updated Aug 7, 2026. Interested? Apply on the company site.

About Sciforium

Byte-Native AI Models & Infrastructure

Sciforium is pioneering the future of AI infrastructure and research. We're developing byte-native multimodal foundation models while delivering serverless LLM serving at a fraction of traditional costs

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