Machine Learning Engineer — Distillation

About the Role

We’re looking for a Machine Learning Engineer focused on model distillation to help us build smaller, faster, and more efficient models without sacrificing quality. You’ll work at the intersection of research and production—taking cutting-edge techniques and turning them into systems that scale.

This is a hands-on role with real ownership: you’ll design distillation pipelines, run large-scale experiments, and ship models used in production.

What You’ll Do

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

What We’re Looking For

• 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
• Pragmatic mindset: you care about shipping, not just papers

Nice to Have

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

Why Join

• Work on core model quality and cost efficiency—not side projects
• High ownership and direct impact on product and roadmap
• Small, senior team with strong research + engineering culture
• Competitive compensation + meaningful equity
• Remote-friendly, async-first environment