AI Research Engineer (Model Compression & Quantization)
Tether Operations Limited5 months ago
Remote, WorldwideSenior
Responsibilities
- Apply low-bit quantization to LLMs, VLMs, and multimodal generative AI 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 methods to remove redundant parameters and attention heads while preserving task performance.
- Analyze trade-offs among model size, latency, memory, computational overhead, and accuracy across compression approaches.
- Research and apply mixed-precision quantization, adaptive pruning schedules, intermediate feature matching, and other advanced compression strategies.
- Build robust model-compression pipelines and address bottlenecks in production inference for edge devices.
- Establish performance and fidelity metrics for compressed models.
- Stay current with research in compression for multimodal and generative architectures.
- Document methodologies, experiments, and results to support reproducibility, collaboration, and stakeholder communication.
- Author technical papers and publish findings in leading AI and machine learning conferences.
Requirements
- Bachelor’s degree in Computer Science or a related field.
- Ideally, a PhD in NLP, Machine Learning, or a related field, with a strong AI R&D track record and publications in A* conferences.
- Experience with PyTorch or an equivalent deep learning framework.
- Hands-on experience with both Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ).
- Research and hands-on experience applying knowledge distillation to compress large models into smaller, efficient models.
- Research and hands-on experience applying model pruning to compress large models into smaller, efficient models.
- Strong understanding of neural network architectures and training, including transformers, LLMs, VLMs, backpropagation, optimization, and fine-tuning.
- Familiarity with C++ is preferred, particularly for low-level quantization kernels or inference optimizations.
- Excellent English communication skills.
Benefits
- Remote work with a globally distributed team.
- Opportunity to contribute to AI research and publish findings at top-tier conferences.
- Work on multimodal AI systems intended for efficient deployment on resource-constrained edge devices.
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.