Senior AI Serving Engineer, Backend

Reposted 2 Days Ago
San Francisco, CA, USA
In-Office
190K-250K Annually
Mid level
Artificial Intelligence • Information Technology • Software
The Role
Develop the model serving platform for multimodal AI models, optimizing performance, collaborating with researchers, and ensuring reliable production infrastructure.
Summary Generated by Built In

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.


About the role

This role offers a unique opportunity to work on the core systems that power Sciforium’s multimodal AI models. You’ll help build the model serving platform working across C++, Python, runtime execution, and distributed infrastructure to create a fast, reliable engine for real-time AI applications.

You’ll gain hands-on experience with performance engineering, learn how large AI models are optimized and deployed at scale, and collaborate closely with ML researchers and experienced systems engineers. If you enjoy low-level programming, care deeply about performance, and want exposure to the full AI stack, this role provides both high-impact work and strong growth potential.

What you'll do
  • 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.

Ideal candidate profile
  • 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.

Nice-to-have
  • Experience with ML systems engineering, open source inference engine like vLLM, Sglang, or TRT-LLM

  • Proficiency in CUDA or ROCm and experience with GPU profiling tools

  • Contributions to open-source ML or HPC infrastructure

Benefits include
  • Medical, dental, and vision insurance

  • 401k plan

  • Daily lunch, snacks, and beverages

  • Flexible time off

  • Competitive salary and equity

Equal opportunity

Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Skills Required

  • 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
  • Comfortable working from the office and contributing to a fast-moving, high-ownership team culture
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The Company
HQ: San Francisco, CA
7 Employees
Year Founded: 2024

What We Do

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

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