Furiosa Ai

Furiosa Ai

AI Application Engineer

Company

Furiosa Ai

Role

AI Application Engineer

Job type

Full-time

Posted

Yesterday

Salary

Not disclosed by employer

Job description

ABOUT THE JOB

For the successful enabling of RNGD's global developer community, the AI Application Engineer will bridge the gap between FuriosaAI's software stack and market demand by developing a comprehensive AI application ecosystem, with high-impact AI applications and technical resources, centered around our FuriosaAI apps repository.

  • Furiosa-apps Repository: https://github.com/furiosa-ai/furiosa-apps

RESPONSIBILITIES

  • Model Enablement: Lead the integration of diverse AI models including VLA, Vision, and Multimodal architectures by utilizing our kernel programming model to ensure performance and developer readiness.
  • Reference Application Development: Build end-to-end applications such as RAG systems, agentic systems, RL inference engines, and Video processing pipelines by incorporating top-stack toolchains and industry-standard frameworks.
  • Ecosystem Development: Support live application demos and collaborate with the global developer community through GitHub and open-source channels to improve real-world usability.
  • Industry Leadership: Identify and prepare strategic contribution items for industry groups such as OCP to establish FuriosaAI’s technical leadership and market presence.

MINIMUM QUALIFICATIONS

  • BS in Computer Science, Electrical Engineering, or a related field.
  • Experience in programming with Python, Rust, or other programming languages.
  • Experience in AI model implementation, optimization, and deployment using deep learning frameworks (PyTorch, HuggingFace), serving frameworks (vLLM, SGLang), and performance profiling tools.
  • Experience collaborating across engineering, product, and design teams to align software development with product requirements.

PREFERRED QUALIFICATIONS

  • MS or PhD in Computer Science, Electrical Engineering, or a related field.
  • Experience in low-level systems programming or developing high-performance kernels for AI accelerators.
  • Experience in open-source or research projects on AI applications such as agentic systems, RAG, or multimodal pipelines.
  • Experience in strategic technical leadership, including authoring architectural guides or delivering technical presentations to industry-standard groups such as OCP.
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