AI Research Engineer (Model Compression & Quantization)
Tether Operations Limited5 months ago
Remote, WorldwideSenior
Responsibilities
- Apply low-bit and mixed-precision quantization to LLMs, VLMs, and other multimodal generative AI models while preserving accuracy and output quality.
- Use knowledge distillation to transfer capabilities from large teacher models to smaller student models for efficient multimodal reasoning across text, image, and audio inputs.
- Implement pruning methods to remove redundant parameters and attention heads while maintaining task performance.
- Build compression pipelines and analyze trade-offs among model size, latency, memory, throughput, and accuracy.
- Research and apply advanced compression methods, including adaptive pruning schedules and distillation with intermediate feature matching.
- Establish performance and fidelity metrics and address production inference bottlenecks for resource-constrained edge devices.
- Track current research in model compression for multimodal and generative architectures.
- Document methodologies, experiments, and results to support reproducibility, collaboration, and stakeholder communication.
- Author technical papers and publish model-compression research in leading conferences such as NeurIPS, ICML, ICLR, CVPR, ACL, and AAAI.
Requirements
- Bachelor's degree in Computer Science or a related field.
- Strong background in AI research and development, with deep expertise in model compression and multimodal model architectures.
- Hands-on experience with PyTorch or an equivalent deep learning framework.
- Hands-on experience with both Quantization-Aware Training and Post-Training Quantization.
- Research and practical experience using knowledge distillation to compress large models into smaller, efficient models.
- Research and practical experience using model pruning to reduce model size and computational overhead.
- Solid understanding of neural network architectures and training, including transformers, LLMs, VLMs, backpropagation, optimization, and fine-tuning.
- A PhD in NLP, Machine Learning, or a related field and a strong publication record at top-tier AI conferences are preferred.
- Familiarity with C++ is a plus, particularly for low-level quantization kernels or inference optimizations.
Benefits
- Remote work with a globally distributed team.
- Opportunity to work on advanced multimodal AI, model compression, and efficient edge deployment in a fintech and digital-asset company.
Categories
AI ResearchML Engineering
About Tether Operations Limited
Tether Operations Limited develops and issues reserve-backed digital tokens used by exchanges, wallets, payment processors, and merchants, most notably the USDT stablecoin available across multiple blockchains. Founded in 2014 and privately held, it earns revenue from token issuance, reserve management, and related services, and has expanded into tokenization, peer-to-peer communications (Keet), and Bitcoin mining. USDT is widely used in crypto trading and cross-border transfers, including in emerging markets.