Machine Learning Engineer

Responsibilities

• Design and implement knowledge distillation pipelines including teacher–student, self-distillation, and multi-teacher methods
• Distill large foundation models into smaller, faster, and more cost-effective models for inference
• Run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs
• Collaborate with research teams to translate new distillation ideas into production-ready code
• Optimize training and inference performance regarding memory, throughput, and 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 for 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 focused on shipping production-ready code

Preferred Qualifications

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

About the Company

We are a small, senior team with a strong research and engineering culture, working on core model quality and cost efficiency in a remote-friendly, async-first environment.