Ai Distilation Expert

About the Role We’re building a real-time generative AI system where latency is the core challenge. We’re looking for a model distillation specialist to help us shrink large models into fast, deployment-ready versions without sacrificing quality. This is a hands-on role: you’ll design and run distillation experiments, evaluate quality/latency tradeoffs, and ship the results to production GPU infrastructure. What You’ll Do Design and run knowledge distillation pipelines – teacher/student setups for large neural models (LLMs and/or generative vision models) Optimize for latency – reduce inference cost via distillation, quantization, pruning, and mixed precision Evaluate rigorously – build empirical quality-vs-latency comparisons and A/B test variants Own training loops – write and debug full PyTorch training code, not just run existing scripts Ship to production – deploy distilled models on live GPU servers and measure real-world performance Must Have 2 years hands-on PyTorch , including training/fine-tuning models from scratch – not just serving pretrained ones Proven experience with model distillation – you’ve actually distilled a model and measured the results Strong grasp of model compression techniques : quantization, pruning, knowledge distillation, LoRA/parameter-efficient methods Solid understanding of loss design for distillation (KL divergence, feature/logit matching, perceptual losses) Comfortable reading and extending dense, lightly-documented research code Strong Plus Experience distilling autoregressive LLMs or speech models Experience with GANs, diffusion, or flow-matching models GPU inference optimization: torch.compile, CUDA memory management, multi-GPU setups Familiarity with streaming/real-time ML systems Working Style We operate like a research lab: empirical, fast iteration, incomplete docs, self-directed testing. You’ll need to be comfortable designing your own experiments and pushing for more engineering structure as you go. #J-18808-Ljbffr RB Labs