Furiosa Ai
AI Application Engineer
3mo ago
Worldwidepythonrustpytorch
Develop and lead AI application ecosystem development, integrating AI models and supporting global developer community.
Responsibilities
- 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
- 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.
Requirements
- 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.
Nice to have
- 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.