Machine Learning Engineer — Distillation

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer — Distillation based in Spain.

This role offers the opportunity to advance the efficiency and scalability of next-generation machine learning systems.
You will work at the intersection of research and production, transforming cutting-edge model optimization techniques into real-world solutions.
The position focuses on building smaller, faster, and more cost-effective AI models while maintaining high-quality performance.
You will design advanced distillation pipelines, run large-scale experiments, and contribute directly to production systems.
This is an opportunity for an ML engineer who enjoys deep technical challenges, experimentation, and practical innovation.
You will join a collaborative environment where your work directly influences model quality, performance, and product impact.

As a Machine Learning Engineer focused on Distillation, you will design, develop, and optimize machine learning systems that improve model efficiency without compromising performance. You will combine research expertise with engineering execution to build scalable AI solutions.

• Design and implement advanced knowledge distillation pipelines, including teacher-student approaches, self-distillation, and multi-teacher architectures.
• Distill large foundation models into smaller, faster, and more efficient models optimized for production inference.
• Run large-scale machine learning experiments to evaluate model quality, latency, efficiency, and cost tradeoffs.
• Analyze experimental results and use insights to improve model performance and optimization strategies.
• Collaborate with research teams to transform emerging distillation techniques into reliable production-ready implementations.
• Optimize training and inference performance, including memory usage, throughput, latency, and computational efficiency.
• Develop and improve internal tools, evaluation frameworks, and experiment tracking systems.
• Contribute to improving machine learning workflows and engineering best practices.
• Explore opportunities to contribute to open-source models, research initiatives, or technical tooling.

Requirements

The ideal candidate is a machine learning engineer with strong experience in deep learning, model optimization, and production-oriented AI development. You should have hands-on experience with distillation techniques and the ability to balance research innovation with practical engineering delivery.

• Strong background in machine learning, deep learning, and neural network architectures.
• Hands-on experience implementing model distillation techniques for large language models or other neural networks.
• Solid understanding of training dynamics, optimization methods, loss functions, and model evaluation.
• Experience working with PyTorch, JAX, or similar modern machine learning frameworks.
• Experience running experiments in multi-GPU or distributed training environments.
• Ability to evaluate and optimize tradeoffs between model quality, performance, latency, and cost.
• Strong programming and software engineering skills with the ability to build production-ready ML systems.
• Practical mindset focused on shipping impactful solutions rather than only theoretical research.
• Experience with inference optimization techniques such as quantization, pruning, or kernel optimization is a plus.
• Familiarity with language model evaluation methodologies is preferred.
• Open-source contributions, research publications, or experience in fast-moving startup environments are considered valuable.

Benefits

• Competitive compensation package with meaningful equity opportunities.
• Opportunity to work on core machine learning systems that directly impact product performance and efficiency.
• High ownership role with significant influence over technical direction and roadmap.
• Collaboration with a small, senior team combining research expertise and engineering excellence.
• Remote-friendly work environment with an async-first culture.
• Opportunity to solve challenging AI optimization problems at scale.
• Ability to contribute to advanced model development and emerging AI technologies.
• Fast-paced environment that encourages innovation, experimentation, and technical growth.