Featherless AI
Machine Learning Engineer — Distillation
Rol remoto de Machine Learning Engineer con fit claro de ubicación del candidato.
Publicado25 jul 2026
Países elegibles7 países aceptados
Señal de seniorityMiddle
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
Machine Learning Engineer — Distillation
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
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 haves
- 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
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