Machine Learning Engineer - Distillation

Overview

This opportunity is offered on behalf of a partner company located in Ireland, seeking a Machine Learning Engineer specialized in Distillation. The role involves advancing scalable and efficient next-gen machine learning systems by translating state-of-the-art model optimization strategies into practical, production-ready solutions.

Key Responsibilities

• Architect and deploy sophisticated knowledge distillation pipelines using teacher-student, self-distillation, and multi-teacher models.
• Convert large-scale foundation models into smaller, high-performance, and cost-efficient variants tailored for production inference.
• Conduct extensive machine learning experiments to assess parameters such as model accuracy, latency, efficiency, and associated costs.
• Interpret experimental data to enhance model performance and refine optimization methodologies.
• Collaborate closely with research teams to migrate innovative distillation techniques into robust, production-level systems.
• Improve training and inference workflows focusing on memory management, throughput, latency reduction, and computational efficiency.
• Create and upgrade tools for internal evaluation, experimentation tracking, and overall machine learning pipeline improvements.
• Participate in initiatives related to open-source contributions, research collaborations, or development of advanced technical tools.

Candidate Requirements

• In-depth knowledge of machine learning, deep learning, and neural network design.
• Practical experience implementing model distillation methods on large language models or comparable neural network architectures.
• Firm grasp of training processes, optimization strategies, loss function applications, and model evaluation techniques.
• Proficiency with modern ML frameworks such as PyTorch or JAX.
• Experience executing multi-GPU or distributed training experiments.
• Capability to analyze and optimize trade-offs between model quality, latency, performance, and operational costs.
• Strong programming and software engineering aptitude to develop scalable, production-quality ML systems.
• Focus on delivering actionable, real-world solutions rather than purely academic research.
• Knowledge of inference optimization approaches like quantization, pruning, or kernel-level improvements is advantageous.
• Understanding of language model assessment methods is a plus.
• Previous open-source engagement, publications, or startup experience are valued assets.

Benefits

• Competitive remuneration accompanied by significant equity participation.
• Role offers direct impact on core machine learning components influencing product efficiency and effectiveness.
• High level of ownership with clear influence on technical strategies and future development plans.
• Collaborate within a compact, expert team blending research excellence with engineering prowess.
• Flexible remote working environment fostering asynchronous communication culture.
• Engage in challenging large-scale AI optimization endeavors.
• Opportunities to advance work in cutting-edge AI model creation and explorations of emerging technologies.
• Dynamic, rapid-paced workplace promoting innovation, experimentation, and professional growth.

Additional Information

This job is facilitated through an AI-enhanced matchmaking system enabling swift, fair, and unbiased candidate evaluation aligned with role requirements. Subsequent application proceedings and hiring decisions are managed exclusively by the partner company's internal team. The recruitment process involves some AI-assisted stages for application and resume analysis; however, human judgment remains paramount. The employer complies with data privacy laws such as GDPR with respect to candidate data handling.