AI Research Engineer (Model Compression & Quantization)
Tether Operations Limited4 months ago
Remote, WorldwideSenior
Responsibilities
- Apply low-bit quantization to LLMs, VLMs, and other multimodal generative AI models while maintaining accuracy and output quality.
- Use knowledge distillation to transfer capabilities from larger teacher models to smaller student models across text, image, and audio inputs.
- Implement pruning methods that remove redundant parameters and attention heads while preserving task performance.
- Evaluate trade-offs among model size, latency, memory usage, throughput, and accuracy, and propose improvements based on empirical results.
- Research and apply mixed-precision quantization, adaptive pruning schedules, intermediate feature matching, and other advanced compression strategies.
- Build compression pipelines, establish performance and fidelity metrics, and address production inference bottlenecks for 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 model-compression research at top-tier conferences.
Requirements
- Bachelor’s degree in Computer Science or a related field; a PhD in NLP, Machine Learning, or a related field is preferred.
- Solid track record in AI research and development, ideally including publications at leading conferences.
- Hands-on experience with PyTorch or an equivalent deep learning framework.
- Experience with both Quantization-Aware Training and Post-Training Quantization.
- Research and practical experience using knowledge distillation to compress large models.
- Research and practical experience using model pruning to compress large models.
- Strong understanding of neural network architectures and training, including transformers, LLMs, VLMs, backpropagation, optimization, and fine-tuning.
- C++ experience is preferred, particularly for low-level quantization kernels or inference optimization.
Benefits
- Global remote work with collaboration across locations.
- Opportunity to work on advanced multimodal AI research and efficient edge deployment.
- Opportunity to publish findings at leading research conferences.