---
title: 'Research Engineer at Kog'
canonical: 'https://feeny.ai/job/research-engineer-kog-paris-5ffy8ryxsz1a'
type: 'job'
last_seen: '2026-09-08'
---

# Research Engineer at Kog

- **Company:** Kog
- **Location:** Paris, France
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-06-09
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/kog/364b3702-a7a9-431d-8e70-f8fe3f4d2f01

## Job description

## About Kog

Kog builds the fastest LLM inference engine on standard datacenter GPUs. Our Kog Inference Engine generates 3,000 output tokens per second per request on a single 8× AMD MI300X node and 2,100 on an 8× NVIDIA H200 node (FP16, batch size 1, no speculative decoding).

We co-design the model architecture and the execution engine together. Our Laneformer model uses Delayed Tensor Parallelism (DTP), a novel architecture that restructures the Transformer dependency graph so inter-GPU communication overlaps with computation rather than blocking it.

We pre-trained a 2B-parameter DTP model on 6T tokens on 256 H100 GPUs.

We are a team of 11 people, including 10 engineers and 5 PhDs.

Test it at playground.kog.ai http://playground.kog.ai. Read the technical details on the Kog Labs blog https://blog.kog.ai.

## What you will work on

You will imagine, design, and run experiments to understand how architectural decisions propagate through inference behavior, morph existing open-weight models into architecture variants optimized for speed, and turn findings into measurable gains in generation speed and model quality.

- Design new model architecture variants, including routing strategies, attention mechanisms, and MoE structure, with execution constraints as a first-order design input.
- Extend the Laneformer thesis by exploring inference-aware architectural variants such as DTP, Ladder Residual, and PT-Transformer, and finding what compounds at scale.
- Own the post-training pipeline across fine-tuning, evaluation methodology, and adaptation of existing open-weight models toward architecture variants optimized for inference speed.
- Scale the stack to large MoE models such as DeepSeek v4 and Qwen 3, working through routing, expert parallelism, and communication patterns at inference time.
- Write up findings as research papers, submit them to top venues, and present them at conferences.
- Contribute to building AI agents that will perform architecture research and training experiments autonomously, starting from the research foundations we are building now.

## What we look for

- You have designed or changed model architecture, where the structure itself was the object of the work. Showing that work, a paper, a repository, or a thesis, is a requirement to move forward.
- You reason about model design and hardware together, tracing how communication structure and layer dependencies shape inference behavior, with fluency in Transformers and MoE deep enough to weigh trade-offs.
- Stronger signals include inference-aware architectural variants such as DTP, Ladder Residual, or PT-Transformer, and post-training methods such as fine-tuning, preference optimization, or quantization, including at research scale.
- A top engineering school or a PhD with concrete architecture work counts, even without industry experience.

## What we offer

- Direct access to AMD and NVIDIA datacenter GPUs from day one
- A team where creativity and technical judgment carry weight and where the people closest to the problem shape the key decisions
- Problems that sit on the critical path of model execution speed and that directly influence what the system can become
- A remote-friendly working model, with one mandatory week per month in our Paris office. Travel and accommodation covered by the company.
- Compensation aligned with top AI research profiles, including equity

## About Kog

## Company Overview
- **One-liner**: Kog builds a realtime AI inference engine that achieves up to 3,000 tokens per second per request on standard datacenter GPUs through low‑level GPU engineering and model‑architecture co‑design.
- **Entity Type**: Private (seed‑stage startup)
- **Headquarters**: Paris, France
- **Founded**: 2023
- **Founders**: Gaël Delalleau

## Core Business
- **Primary industry**: AI infrastructure / GPU inference
- **Target customers**: B2B – GPU providers, AI labs, enterprises deploying AI agents and agentic workflows
- **Mission statement**: "Power every digital experience by providing a vertically integrated, realtime AI inference engine."

## Products & Services
- **Kog Inference Engine (KIE)**: A drop‑in replacement for vLLM and TensorRT‑LLM. Uses a monokernel runtime (single persistent GPU program), custom collective‑communication library (KCCL), and hand‑crafted assembly‑level GPU code (CUDA/PTX, HIP/CDNA) – no dependency on PyTorch, Triton, or vendor libraries on the hot path.
- **Kog LaneFormer**: A model architecture that implements Delayed Tensor Parallelism (DTP), overlapping cross‑device communication with computation to hide latency in multi‑GPU decoding.
- **Kog Communication Library (KCCL)**: A latency‑optimized (<3 μs) inter‑GPU communication layer tuned at the assembly level for each GPU architecture, replacing RCCL/NCCL.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key metric**: Total funding – $5M raised (Seed round led by Varsity VC with BPI France Deep Tech Program)
- **Notable investors**: Varsity VC, BPI France; awarded the French Tech 2030 label (October 2025)
- **Growth signals**: Launched public tech preview on AMD MI300X in May 2026 reaching 3,000 tok/s per request; 2,100 tok/s on NVIDIA H200; up to 3.5× faster than vLLM on AMD Instinct; team of 14 with 10 engineers/researchers (5 PhDs); press coverage from French Tech Journal.

## Competitive Advantages
- **Hardware‑software co‑design**: Every layer is optimised for uninterrupted compute – monokernel eliminates kernel‑launch overhead and host‑side scheduling.
- **Custom low‑level stack**: Avoids general‑purpose frameworks (PyTorch, Triton, CUTLASS, etc.) on the critical path; uses hand‑tuned assembly for each GPU.
- **Delayed Tensor Parallelism (DTP)**: Novel architecture that hides all‑reduce communication behind computation, crucial for single‑request latency.
- **Speed niche**: Matches dedicated inference‑silicon speeds on standard datacenter GPUs, without lock‑in to proprietary hardware.

## Strategic Focus
- **Current priorities**: Optimising single‑request decode latency for AI agents; expanding support for larger models and batching; targeting sovereign‑AI buyers and enterprise GPU fleets.
- **Direction**: Prove that software optimisations can unlock the full speed of existing GPU hardware, reducing reliance on custom chips.

## Why Work Here
- **Culture**: High‑density team of PhDs and GPU research engineers who “treat hardware limits as a starting point.” Emphasis on deep technical challenges at the intersection of architecture, runtime, and kernel design.
- **Team size**: ~14 people (as of mid‑2026), offering outsized impact per person.
- **Locations**: Headquarters in Paris, with presence in Hungary and Portugal – likely hybrid/remote‑friendly.
- **Perks**: Work on cutting‑edge inference technology with top investors (Varsity VC) and government recognition (French Tech 2030). The company is small enough that engineers shape the entire stack.

## Sources
1. [Kog.ai](https://www.kog.ai/)
2. [LinkedIn](https://fr.linkedin.com/company/kogai)
3. [Kog Blog – Real‑time LLM Inference on Standard GPUs (3,000 tok/s)](https://blog.kog.ai/real-time-llm-inference-on-standard-gpus-3-000-tokens-s-per-request/)
4. [French Tech Journal – AI Industry Spent Billions Chasing Faster Chips](https://www.frenchtechjournal.com/the-ai-industry-spent-billions-chasing-faster-chips-inference-startup-kog-says-they-were-solving-the-wrong-problem/)
5. [GitHub](https://github.com/kog-ai)

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