Remote Machine Learning Engineer — Distillation

Description:

• We are looking for a Machine Learning Engineer focused on model distillation to help us build smaller, faster, and more efficient models without sacrificing quality.
• You will 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, where you will design distillation pipelines, run large-scale experiments, and ship models used in production.
• Your responsibilities will include designing and implementing knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.).
• You will distill large foundation models into smaller, faster, and cheaper models for inference.
• You will run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs.
• Collaboration with research to translate new distillation ideas into production-ready code will be part of your role.
• You will optimize training and inference performance, focusing on memory, throughput, and latency.
• You may also contribute to internal tooling, evaluation frameworks, and experiment tracking.
• Optionally, you can contribute back to open-source models, tooling, or research.

Requirements:

• A strong background in machine learning or deep learning is required.
• Hands-on experience with model distillation, particularly with LLMs or other neural networks, is essential.
• A solid understanding of training dynamics, loss functions, and optimization is necessary.
• Experience with PyTorch (or JAX) and modern ML tooling is required.
• You should be comfortable running experiments on multi-GPU or distributed setups.
• The ability to reason about model quality versus performance tradeoffs is important.
• A pragmatic mindset is needed; you should care about shipping, not just papers.

Benefits:

• You will work on core model quality and cost efficiency, not side projects.
• There is high ownership and direct impact on product and roadmap.
• You will be part of a small, senior team with a strong research and engineering culture.
• Competitive compensation and meaningful equity are offered.
• The work environment is remote-friendly and async-first.