AI Research Engineer (Model Compression & Quantization)

Tether.io; España; On-site; PHP 4,914,004 - 7,371,007; Full time

Job summary

Tether.io seeks an AI Research Member to innovate in model compression and deployment for advanced multimodal AI systems, including large language models. Candidates with experience in quantization, knowledge distillation, and pruning are encouraged to apply.

A degree in Computer Science or related field is preferred. This role requires staying updated with the latest research and authoring papers to contribute to the field. Join Tether.io and enhance the efficiency of AI across edge devices.

Qualifications

• Degree in Computer Science or related field; Ph.D. in NLP, Machine Learning, or a related field preferred.
• Solid track record in AI R&D with publications in A* conferences.
• Experience with PyTorch deep learning frameworks or equivalent.
• Hands-on experience with model quantization (both QAT and PTQ).
• Hands-on experience with knowledge distillation for compressing large models into smaller, efficient ones.
• Hands-on experience with model pruning for compressing large models into smaller, efficient ones.
• Solid understanding of neural network architectures and training processes, including transformers (LLMs, VLMs), backpropagation, optimization, and fine-tuning.
• Familiarity with C++ is a plus, especially for implementing low-level quantization kernels or inference optimizations.

Responsibilities

• Apply low-bit quantization to reduce model size and inference latency for generative AI models while maintaining accuracy.
• Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models.
• Implement pruning techniques to remove redundant parameters and attention heads.
• Analyze trade-offs between model efficiency and accuracy across techniques.
• Research and apply mixed-precision quantization and other advanced compression strategies.
• Stay current with the latest research in model compression for multimodal architectures.
• Document methodologies, experiments, and results clearly.
• Author technical papers to advance the field of model compression.

Job description

About The Job

As a member of our AI research team, you will drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). Your work will focus on reducing model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource‑constrained edge devices. You will apply and advance compression techniques such as quantization, knowledge distillation, and pruning to streamline complex multimodal architectures that integrate text, images, and audio.

Responsibilities

• Apply low‑bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality.
• Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs.
• Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance.
• Analyze trade‑offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings.
• Research and apply mixed‑precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules, distillation with intermediate feature matching) to optimize the accuracy–performance balance.
• Stay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures.
• Document methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication.
• Author technical papers and publish findings in top‑tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI.

Qualifications

• Degree in Computer Science or related field; Ph.D. in NLP, Machine Learning, or a related field preferred.
• Solid track record in AI R&D with publications in A* conferences.
• Experience with PyTorch deep learning frameworks or equivalent.
• Hands‑on experience with model quantization (both QAT and PTQ).
• Hands‑on experience with knowledge distillation for compressing large models into smaller, efficient ones.
• Hands‑on experience with model pruning for compressing large models into smaller, efficient ones.
• Solid understanding of neural network architectures and training processes, including transformers (LLMs, VLMs), backpropagation, optimization, and fine‑tuning.
• Familiarity with C++ is a plus, especially for implementing low‑level quantization kernels or inference optimizations.