AI Researcher — Distillation

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.