AI Inference Engineer – QVAC

Tether.to

. Own the inference backbone behind QVAC’s local AI stack .

Posted 11/9/2026full-timeRemoteMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in C++ programming and optimization for AI inference systems, with a strong understanding of deep learning concepts and experience deploying models to edge devices. Proven ability to collaborate with researchers and integrate AI features into existing products while ensuring system stability and performance.

Highest-signal resume keywords

C++ ProgrammingLlama.cpp Inference EngineGPU Frameworks (CUDA, Vulkan, Metal, OpenCL)Deep Learning ConceptsModel Productionization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

C++ ProgrammingDeep Learning ConceptsTransformersLLMsDiffusion ModelsModel TrainingModel Fine-TuningDistributed SystemsJavaScript

Soft Skills

CollaborationRapid Assimilation of New Technologies

Tools & Technologies

Llama.cppGgmlCUDAVulkanMetalOpenCL

Industry Keywords

AI R&DInference LayerModel ExecutionEdge Devices

Tech Stack

Tools & technologies

Distributed SystemsJavaScriptC++

About the role

Key responsibilities & impact

Own the inference backbone behind QVAC’s local AI stack
Engineer the C++ systems layer for fast, reliable, and predictable model execution on user hardware
Optimize startup behavior, memory pressure, throughput, latency, and long-session stability
Define and evolve core abstractions for inference features
Deploy machine learning models to edge devices using llama.cpp and ggml
Port and enhance inference engines to run efficiently across different hardware
Collaborate with researchers on coding, training, and transitioning models from research to production
Integrate AI features into existing products
Ensure the inference layer is stable, optimized, and ready for stack integration

Requirements

What you’ll need

Strong programming skills in C++
Strong experience with Llama.cpp and ggml inference engines
Experience with at least one GPU framework: CUDA, Vulkan, Metal, or OpenCL
Good understanding of deep learning concepts and model architectures
Experience with transformers, LLMs, and diffusion models
Demonstrated ability to rapidly assimilate new technologies and techniques
A degree in Computer Science, AI, Machine Learning, or a related field
Solid track record in AI R&D
Knowledge of training or fine-tuning LLMs is a bonus
Experience productionizing models is a bonus
Research experience in new model architectures is a bonus
Experience with distributed systems is a bonus
JavaScript experience is a bonus

Benefits

Comp & perks

Remote work from anywhere in the world
Opportunity to collaborate with a global remote team
Work on private, on-device AI experiences and peer-to-peer AI products
Opportunity to contribute to innovative fintech, blockchain, AI, and digital asset products