Machine Learning Engineer — Distillation

Stack

Responsibilities

• ▹Design and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher)
• ▹Distill large foundation models into smaller, faster, cheaper models for inference
• ▹Run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs
• ▹Translate new distillation ideas into production-ready code with research
• ▹Optimize training and inference performance (memory, throughput, latency)
• ▹Contribute to internal tooling, evaluation frameworks, and experiment tracking

Requirements

• ▹Strong background in machine learning or deep learning
• ▹Hands-on experience with model distillation (LLMs or other neural networks)
• ▹Solid understanding of training dynamics, loss functions, and optimization
• ▹Experience with PyTorch (or JAX) and modern ML tooling
• ▹Comfort running experiments on multi-GPU or distributed setups
• ▹Ability to reason about model quality vs. performance tradeoffs

Nice to have

• ▹Experience distilling LLMs or large sequence models
• ▹Inference optimization experience (quantization, pruning, kernels)
• ▹Familiarity with evaluation for language models
• ▹Open-source contributions or research publications
• ▹Experience in early-stage or fast-moving startups

Soft skills

What we offer

• ▹Real ownership and direct impact on product and roadmap
• ▹Small, senior team with strong research and engineering culture
• ▹Competitive compensation and meaningful equity
• ▹Remote-friendly, async-first environment