AI Research Engineer (Model Compression & Quantization)

h3Join Tether and Shape the Future of Digital Finance /h3 pAt Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. /p h3Innovate with Tether /h3 h3Tether Finance: /h3 pOur innovative product suite features the world’s most trusted stablecoin, bUSDT /b, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. /p pBut that’s just the beginning: /p h3Tether Power: /h3 pDriving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. /p h3Tether Data: /h3 pFueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like bKEET /b, our flagship app that redefines secure and private data sharing. /p h3Tether Education /h3 pDemocratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. /p h3Tether Evolution /h3 pAt the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. /p h3Why Join Us? /h3 pOur team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. /p pIf you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. /p h3Are you ready to be part of the future? /h3 h3About the job /h3 pAs 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. /p pWe expect you to have deep expertise in model compression methods and a strong background in multimodal model architectures. You will adopt a hands‑on, research-driven approach to develop, test, and implement novel compression strategies that balance model size, latency, throughput, and accuracy. Your responsibilities include building robust compression pipelines, establishing performance and fidelity metrics, and addressing bottlenecks in production inference. The ultimate goal is to deliver scalable, low-memory, low‑latency AI systems on edge devices (i.e., smartphones) that maintain high fidelity and tangible real-world value. /p h3Responsibilities /h3 ul liApply low-bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality. /li liLeverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs. /li liImplement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance. /li liAnalyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings. /li liResearch 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. /li liStay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures. /li liDocument methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication. /li liAuthor 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. /li /ul h3Qualifications /h3 ul liA 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 RD (with good publications in A* conferences). /li liExperience with PyTorch deep learning frameworks or equivalent frameworks. /li liHands‑on experience with model quantization including both Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ). /li liResearch and hands‑on experience with knowledge distillation for compressing large models into smaller, efficient ones. /li liResearch and hands‑on experience with model pruning for compressing large models into smaller, efficient ones. /li liSolid understanding of neural network architectures and training processes – Including transformers (e.g., LLMs, VLMs), backpropagation, optimization, and fine-tuning techniques. /li liFamiliarity with C++ is a plus (especially for implementing low‑level quantization kernels or inference optimisations). /li /ul h3Important information for candidates /h3 pRecruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles: /p ul libApply only through our official channels. /b We do not use third‑party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: /li libVerify the recruiter’s identity. /b 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. /li libBe cautious of unusual communication methods. /b We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. /li libDouble-check email addresses. /b All communication from us will come from emails ending in b@ /btether.tob or@ /btether.io. /li libWe will never request payment or financial details. /b If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. /li /ul pbWhen in doubt, feel free to reach out through our official website. /b /p #J-18808-Ljbffr