Machine Learning Engineer — Distillation

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.

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  Design and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.)

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  Distill large foundation models into smaller, faster, and cheaper models for inference

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  Run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs

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  Collaborate with research to translate new distillation ideas into production-ready code

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  Optimize training and inference performance (memory, throughput, latency)

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  Contribute to internal tooling, evaluation frameworks, and experiment tracking

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  (Optional) Contribute back to open-source models, tooling, or research

• 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

• 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