AI Research Engineer (Model Compression & Quantization)
Tether Operations Limited4 months ago
Remote, WorldwideSenior
Responsibilities
- Apply low-bit quantization to LLMs, VLMs, and other 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 techniques to remove redundant parameters and attention heads without sacrificing task performance.
- Analyze trade-offs among model size, latency, memory, 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 robust compression pipelines and establish performance and fidelity metrics for production inference.
- Stay current with model-compression research 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 research conferences.
Requirements
- Bachelor's degree or higher 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, including strong publications in top-tier conferences.
- Experience with PyTorch or an equivalent deep learning framework.
- Hands-on experience with 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 processes, including transformers, LLMs, VLMs, backpropagation, optimization, and fine-tuning.
- Familiarity with C++ is preferred, particularly for low-level quantization kernels or inference optimizations.
Benefits
- Remote work from locations around the world.
Categories
AI ResearchML Engineering