AI Research Engineer (Model Compression & Quantization)

4 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 across text, image, and audio inputs.
Implement pruning methods to remove redundant parameters and attention heads while maintaining task performance.
Build compression pipelines and establish metrics for model size, latency, throughput, memory use, accuracy, and fidelity.
Analyze efficiency-versus-accuracy trade-offs and propose improvements based on empirical results.
Research advanced 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.
Document methodologies, experiments, and results to support reproducibility and collaboration.
Author technical papers and publish model-compression research at leading AI conferences.

Degree in Computer Science or a related field.
Strong AI R&D background with an excellent publication record, preferably including A* conference publications.
Hands-on 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 model compression.
Research and hands-on experience with model pruning for model compression.
Strong 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 is preferred.
Familiarity with C++ is a plus, particularly for low-level quantization kernels or inference optimization.

Remote work with a globally distributed team.
Opportunity to work on multimodal AI compression and efficient edge deployment in a digital-finance technology company.
Research and publication opportunities at leading conferences.

201-500 employees