Machine Learning Engineer - Distillation

About the Role

This opportunity is presented on behalf of a partner company based in Germany seeking a Machine Learning Engineer specializing in Distillation. The role focuses on enhancing the efficiency and scalability of cutting-edge machine learning systems by developing optimized AI models that are smaller, faster, and more cost-effective without losing quality.

The position bridges research and practical deployment, involving the design of advanced distillation pipelines, execution of large-scale experiments, and contributions to production-level AI systems. The candidate will work within a collaborative environment that prioritizes impactful technical innovation and model performance.

Core Responsibilities

• Design and develop sophisticated knowledge distillation workflows, including teacher-student models, self-distillation, and multi-teacher configurations.
• Transform large foundational models into efficient versions optimized for production inference.
• Conduct extensive machine learning experiments evaluating metrics like quality, latency, efficiency, and cost.
• Analyze experiment outcomes to refine model optimization approaches and performance.
• Collaborate closely with research teams to convert emerging distillation methods into stable, production-ready implementations.
• Enhance training and inference efficiency regarding memory consumption, throughput, latency, and computation.
• Develop and maintain internal tools, evaluation frameworks, and experiment tracking systems.
• Contribute to advancing machine learning processes and engineering standards within the organization.
• Explore opportunities for involvement in open-source projects, research initiatives, or development of technical tooling.

Required Qualifications and Skills

• Comprehensive understanding of machine learning, deep learning, and neural network design.
• Practical experience implementing distillation methods on large neural networks such as language models.
• Strong grasp of training mechanics, optimization techniques, loss functions, and model evaluation criteria.
• Proficiency with contemporary ML frameworks like PyTorch and JAX.
• Experience executing experiments using multi-GPU or distributed training systems.
• Adept at balancing model performance, latency, quality, and cost factors.
• Excellent programming and software engineering capabilities suitable for deploying production-grade ML applications.
• Focused on delivering actionable, real-world solutions rather than solely theoretical exploration.
• Familiarity with inference optimization strategies such as quantization, pruning, or kernel-level improvements is advantageous.
• Preferred experience includes language model evaluation techniques, open-source contributions, research publications, or fast-paced startup environments.

Benefits

• Competitive remuneration complemented by meaningful equity participation.
• Engagement in critical ML system development directly influencing product efficiency and quality.
• A role offering high ownership and significant impact on the team’s technical roadmap.
• Collaboration within a small, expert group combining research insight and engineering best practices.
• Remote-first workplace culture supporting asynchronous communication.
• Opportunity to tackle complex AI optimization challenges at scale.
• Chance to contribute to innovative model architectures and emerging AI technologies.
• An environment fostering rapid innovation, experimentation, and technical advancement.

Additional Information

This recruitment process utilizes AI-enabled candidate matching to ensure prompt, objective assessment against key criteria. Final hiring decisions and subsequent steps such as interviews and assessments are handled internally by the hiring organization.

Applicants acknowledge data processing compliant with GDPR and other relevant data protection legislation, including rights to access, correction, erasure, and objection. AI tools assist in application evaluations but do not replace human decision-making.