AI Research Engineer (Model Compression & Quantization)

Tether Operations Limited

3d • Remote

About The Position

As a member of Tether’s AI research team, the AI Research Engineer will drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). The role focuses on reducing model footprint and computational cost while preserving accuracy, so high-performance AI can run efficiently on resource-constrained edge devices such as smartphones.

The engineer will apply and advance quantization, knowledge distillation, pruning, and other compression strategies for architectures integrating text, images, and audio. The work involves developing, testing, and implementing research-driven approaches that balance model size, latency, throughput, memory use, accuracy, and output quality. Responsibilities also include building compression pipelines, establishing performance and fidelity metrics, addressing production inference bottlenecks, documenting reproducible results, and publishing research findings.

Requirements

• A degree in Computer Science or a related field.
• Experience with PyTorch or an equivalent deep learning framework.
• Hands-on experience with model quantization, including Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ).
• Research and hands-on experience with knowledge distillation for compressing large models into smaller, efficient models.
• Research and hands-on experience with model pruning for compressing large models into smaller, efficient models.
• Solid understanding of neural network architectures and training processes, including transformers (such as LLMs and VLMs), backpropagation, optimization, and fine-tuning techniques.

Nice To Haves

• A PhD in NLP, Machine Learning, or a related field.
• A solid track record in AI research and development, including good publications in A conferences.
• Familiarity with C++, 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, including LLMs, VLMs, and multimodal models, while maintaining accuracy and output quality.
• Use knowledge distillation to transfer capabilities from larger teacher models to smaller student models for efficient multimodal reasoning across text, image, and audio inputs.
• Implement pruning techniques to remove redundant parameters and attention heads while reducing computational overhead without sacrificing task performance.
• Analyze trade-offs between model efficiency—including size, latency, and 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, such as adaptive pruning schedules and distillation with intermediate feature matching.
• Stay current with research in model compression, including emerging techniques for multimodal and generative architectures.
• Clearly document methodologies, experiments, and results to support reproducibility, collaboration, and stakeholder communication.
• Author technical papers and publish findings in conferences such as NeurIPS, ICML, ICLR, CVPR, ACL, and AAAI.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level