AI Research Engineer (Model Compression & Quantization)

Overview

About The Job

Responsibilities

• Break down the key responsibilities in bullet points.
• It’s helpful to make them actionable and measurable.
• This could also be grouped into categories for more complex roles.
• 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 (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI.
• A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
• Experience with PyTorch deep learning frameworks or equivalent frameworks
• Hands-on experience with model quantization including both Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ).
• Research and hands-on experience with knowledge distillation for compressing large models into smaller, efficient ones.
• Research and 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 (e.g., LLMs, VLMs), backpropagation, optimization, and fine-tuning techniques.
• Familiarity with C++ is a plus (especially for implementing low-level quantization kernels or inference optimizations).
• Important information for candidates
• Recruitment scams have become increasingly common.
• To protect yourself, please keep the following in mind when applying for roles:
• **Apply only through our official channels.** We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/
• **Verify the recruiter’s identity.** All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website.
• **Be cautious of unusual communication methods.** We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.
• **Double-check email addresses.** All communication from us will come from emails ending in **@**tether.to **or @**tether.io
• **We will never request payment or financial details.** If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.
• When in doubt, feel free to reach out through our official website.