AI Researcher — Distillation

Jobgether; Lavamünd; Vor Ort; EUR 128.000 - 192.000; Vollzeit

Benefits dieser Stelle

Ownership of research direction

Publish research

Compute resources for projects

Exposure to LLM distillation

Close collaboration with engineering

Zusammenfassung

Jobgether, acting on behalf of a partner company, is seeking an AI Researcher focused on distillation in Switzerland. Join a highly technical research environment aimed at improving the efficiency and performance of modern AI models.

You will design distillation techniques, run rigorous experiments, publish findings, and collaborate with engineers to deploy scalable results. A PhD or equivalent research background is highly valued; strong PyTorch skills and open-source contributions are a plus.

Qualifikationen

• Strong academic or professional background in machine learning research.
• Hands-on experience with model distillation or related areas.
• Publication experience through conferences or journals.
• Fluency with PyTorch and research-grade experimentation.
• Ability to design rigorous experiments and interpret results.

Aufgaben

• Design, implement, and evaluate advanced distillation techniques.
• Investigate tradeoffs between size, latency, memory, throughput, and accuracy.
• Develop distillation approaches for LLMs and long-context architectures.
• Conduct large-scale experiments with ablations and analyses.
• Translate research into production-ready implementations with engineers.
• Prepare and submit papers to leading ML conferences.

Kenntnisse

Model distillation

PyTorch

Publication experience

Experiment design

Research methodology

Communication skills

Open-source contributions

PhD or equivalent

Ausbildung

PhD or equivalent

Jobbeschreibung

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a AI Researcher — Distillation based in Switzerland.

Join a highly technical research environment focused on advancing the efficiency and performance of modern AI models.

You will research techniques that transform large, resource-intensive models into smaller, faster, and more deployable systems without sacrificing quality.

The role combines fundamental machine learning research with hands-on experimentation and real-world engineering challenges.

You will have the opportunity to explore model distillation across large language models, long-context architectures, and inference-constrained environments.

Your work will move from research ideas and rigorous experiments into production systems with measurable impact.

You will collaborate closely with engineers while contributing to publications, technical research, and potentially open-source projects.

This opportunity is particularly suited to researchers who want meaningful ownership of their work and the ability to see their ideas deployed in practice.

Accountabilities

• Design, implement, and evaluate advanced model distillation techniques, including teacher-student training, self-distillation, layer-wise distillation, and representation matching.
• Investigate the tradeoffs between model size, latency, memory consumption, throughput, and accuracy.
• Develop novel approaches to distilling large language models, long-context or specialized architectures, and models designed for inference-constrained environments.
• Conduct large-scale experiments, ablation studies, and rigorous analysis to validate research hypotheses and identify meaningful improvements.
• Translate research findings into practical implementations and collaborate closely with engineering teams to productionize successful approaches.
• Prepare and submit research papers to leading machine learning conferences and venues such as NeurIPS, ICML, ICLR, and COLM.
• Contribute to internal research documentation, technical articles, and open-source machine learning projects where appropriate.
• Clearly communicate research objectives, methodologies, results, tradeoffs, and limitations to technical stakeholders.

Requirements

• Strong academic or professional background in machine learning research, with a solid understanding of deep learning fundamentals.
• Hands-on experience with model distillation or closely related areas such as model compression, pruning, quantization, or representation learning.
• Demonstrated publication experience through conference or journal papers, workshop publications, or arXiv preprints.
• Strong understanding of optimization, training dynamics, generalization, and modern deep learning methodologies.
• Fluency in PyTorch or an equivalent deep learning framework, with experience conducting research-grade experimentation.
• Ability to design rigorous experiments, interpret results, and critically evaluate research approaches.
• Strong written and verbal communication skills, with the ability to explain complex research ideas and findings clearly.
• Experience with large language model distillation is highly valued.
• Background in efficiency-focused research involving latency, memory, throughput, or related deployment constraints is advantageous.
• Experience with long-context models or non-Transformer architectures is a plus.
• Open-source contributions to machine learning, research tooling, or related projects are beneficial.
• Prior startup or applied research experience is welcome.
• PhD, postdoctoral, academic research, or industry research experience in machine learning or a related field is particularly relevant, though equivalent research backgrounds may also be considered.

Benefits

• Significant ownership and influence over research direction within a Series A-stage environment.
• Strong support for publishing research and pursuing open research initiatives.
• Close feedback loop between research experimentation and real-world production deployment.
• Access to meaningful compute resources and production-scale machine learning problems.
• Opportunity to work on cutting-edge model efficiency and distillation challenges.
• Collaboration within a small, highly technical team with deep expertise across machine learning and systems.
• Opportunity to see research progress from papers and experimental code through to deployed AI systems.
• Exposure to large language models, efficient inference, long-context architectures, and other emerging AI technologies.

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