AI Research Engineer (Model Compression & Quantization)
Tether Operations Limited5 months ago
Remote, WorldwideSenior
Responsibilities
- Apply low-bit and mixed-precision quantization, including Quantization-Aware Training and Post-Training Quantization, to LLMs, VLMs, and other multimodal generative models.
- Use knowledge distillation to transfer capabilities from large teacher models to smaller student models across text, image, and audio inputs.
- Implement pruning methods to remove redundant parameters and attention heads while preserving task performance.
- Build compression pipelines and establish metrics for model size, latency, throughput, memory use, accuracy, and output fidelity.
- Analyze efficiency-versus-accuracy trade-offs and propose improvements based on empirical results.
- Research advanced compression methods such as adaptive pruning schedules and distillation with intermediate feature matching.
- Identify and address production inference bottlenecks for low-memory, low-latency edge deployment.
- Stay current with model-compression research for multimodal and generative architectures.
- Document methodologies, experiments, and results to support reproducibility and collaboration.
- Author technical papers and publish findings at leading conferences such as NeurIPS, ICML, ICLR, CVPR, ACL, and AAAI.
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 top-tier conferences.
- Experience with PyTorch or an equivalent deep learning framework.
- Hands-on experience with model quantization, including Quantization-Aware Training and Post-Training Quantization.
- Research and hands-on experience with knowledge distillation for compressing large models.
- Research and hands-on experience with model pruning for compressing large models.
- Strong understanding of neural network architectures and training, including transformers, LLMs, VLMs, backpropagation, optimization, and fine-tuning.
- 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 and model-compression research in a fintech and digital-asset environment.
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.