Remote Research Engineer: Fast LLM Inference Architecture

Kog; Paris; Híbrido; EUR 90 000 - 140 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Remote-friendly

Paris office week (mandatory)

Travel & accommodation covered

Equity

Resumo da oferta

Kog is building the fastest LLM inference engine on datacenter GPUs, with a focus on architectural innovations and inference-speed optimizations. You will design architecture variants, explore DTP and MoE ideas, and own post-training pipelines for open-weight models, while scaling to large MoE configurations.

This role combines research and engineering to push the limits of speed and quality. You will publish findings, present at conferences, and help develop autonomous AI agents to run

Qualificações

• Demonstrated work on model architecture as a primary object of work (paper, repo or thesis).
• Ability to reason about model design and hardware together, especially Transformers and MoE.
• Experience with inference-aware variants (DTP, Ladder Residual, PT-Transformer) and post-training methods.
• PhD or top engineering school with architecture work counted is highly valued.

Responsabilidades

• Design new model architecture variants with routing, attention, and MoE under execution constraints.
• Extend Laneformer thesis with inference-aware variants and scale findings at scale.
• Own post-training pipeline across fine-tuning, evaluation, and adaptation of open-weight models.
• Scale the stack to large MoE models, addressing routing, expert parallelism, and communication.
• Publish findings as research papers and present at top venues.
• Contribute to autonomous AI agents for architecture research and training experiments.

Conhecimentos

Architectural design

Transformers

MoE architectures

Hardware-aware design

Research publication

Formação académica

Top engineering school or PhD

Descrição da oferta de emprego

Kog is building the fastest LLM inference engine on datacenter GPUs, with a focus on architectural innovations and inference-speed optimizations. You will design architecture variants, explore DTP and MoE ideas, and own post-training pipelines for open-weight models, while scaling to large MoE configurations.

This role combines research and engineering to push the limits of speed and quality. You will publish findings, present at conferences, and help develop autonomous AI agents to run