Senior AI Research Engineer, Model Inference; Remote

Overview

Senior AI Research Engineer, Model Inference (Remote) – Tether.io

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We are looking for an experienced AI Model Engineer with deep expertise in kernel development, model optimization, fine-tuning, and GPU acceleration. The engineer will extend the inference framework to support inference and fine-tuning for Language models with a strong focus on mobile and integrated GPU acceleration (Vulkan).

This role requires hands-on experience with quantization techniques, LoRA architectures, Vulkan backend, and mobile GPU debugging. You will play a critical role in pushing the boundaries of desktop and on-device inference and fine-tuning performance for next-generation SLM/LLMs.

• Implement and optimize custom inference and fine-tuning kernels for small and large language models across multiple hardware backends.
• Implement and optimize full and LoRA fine-tuning for small and large language models across multiple hardware backends.
• Design and extend datatype and precision support (int, float, mixed precision, ternary QTypes, etc.).
• Design, customize, and optimize Vulkan compute shaders for quantized operators and fine-tuning workflows.
• Investigate and resolve GPU acceleration issues on Vulkan and integrated/mobile GPUs.
• Architect and prepare support for advanced quantization techniques to improve efficiency and memory usage.
• Debug and optimize GPU operators (e.g., int8, fp16, fp4, ternary).
• Integrate and validate quantization workflows for training and inference.
• Conduct evaluation and benchmarking (e.g., perplexity testing, fine-tuned adapter performance).
• Conduct GPU testing across desktop and mobile devices.
• Collaborate with research and engineering teams to prototype, benchmark, and scale new model optimization methods.
• Deliver production-grade, efficient language model deployment for mobile and edge use cases.
• Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications. Define clear success metrics such as improved real-world performance, low error rates, robust scalability, and optimal memory usage, with continuous monitoring and iterative refinements.
• Proficiency in C++ and GPU kernel programming.
• Proven expertise in GPU acceleration with Vulkan framework.
• Strong background in quantization and mixed-precision model optimization.
• Experience and expertise in Vulkan compute shader development and customization.
• Familiarity with LoRA fine-tuning and parameter-efficient training methods.
• Ability to debug GPU-specific performance and stability issues on desktop and mobile devices.
• Hands-on experience with mobile GPU acceleration and model inference.
• Familiarity with large language model architectures (e.g., Qwen, Gemma, LLaMA, Falcon, etc.).
• Experience implementing custom backward operators for fine-tuning.
• Experience creating and curating custom datasets for style transfer and domain-specific fine-tuning.
• Demonstrated ability to apply empirical research to overcome challenges in model development and optimization.

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When in doubt, feel free to reach out through our official website.

Not Applicable

Full-time

Information Technology

Technology, Information and Internet