Join Inferact as an inference runtime engineer to innovate AI inference engines for large models in a fully remote role.
Posted by employer 1 week ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
Requirements
Not disclosed in this posting: compensation, years of experience.
Benefits
Joblaze summary
In this role, the inference runtime engineer focuses on enhancing the performance of large language models and diffusion model serving by optimizing the vLLM inference engine for various hardware architectures. Key skills include a strong grasp of transformer architectures, proficiency in Python and PyTorch, and experience with LLM inference systems. This position is ideal for someone with a solid technical background in machine learning and a passion for pushing the boundaries of AI technology. The team is composed of experts who have significantly contributed to the development of vLLM, fostering an innovative environment.
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Quick facts
From the original posting
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.
We're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference.
Minimum qualifications:
Bachelor's degree or equivalent experience in computer science, engineering, or similar.
Deep understanding of transformer architectures and their variants.
Strong programming skills in Python with experience in PyTorch internals.
Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI).
Ability to read and implement model architectures and inference techniques from research papers.
Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases.
Preferred qualifications:
Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving.
Familiarity with RL frameworks and algorithms for LLMs.
Experience with multimodal inference (audio/image/video/text).
Contributions to open-source ML or system infrastructure projects.
Bonus points if you have:
Implemented core features in vLLM or other inference engine projects.
Contributed to vLLM integrations (verl, OpenRLHF, Unsloth, LlamaFactory, etc).
Written widely-shared technical blogs or side projects on vLLM or LLM inference.
Location: Fully remote, worldwide. We're timezone-flexible but expect regular overlap with Pacific Time for critical syncs.
Compensation: We offer competitive compensations (salary + equity) compared to the local market conditions.
Visa sponsorship: We sponsor visas on a case-by-case basis.
Benefits: Inferact offers competitive benefits appropriate to your location, including health coverage where applicable.