# Training / AI Infrastructure at Genesis AI

> Hybrid · United Kingdom · Unspecified · DevOps / SRE / Infra · open. Source: https://alderhq.app/jobs/ashby%3Agenesis%3A2196f682-bd01-4f83-992d-367bbb3c8e8b (read 2026-11-05).

## Facts (observed)

- Company: [Genesis AI](https://alderhq.app/companies/genesis-ai)
- Location text: London
- Where it can be done from (inferred): Hybrid · United Kingdom (evidence: "Location: London")
- Posted: 2026-09-07 (59 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/genesis/2196f682-bd01-4f83-992d-367bbb3c8e8b
- Skills listed (inferred): Node.js, Python, PyTorch, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (London, n 4): p25 £95k, median £101k, p75 £138k

## Description (the employer's text)

### What You’ll Do

Drive down wall-clock time to convergence by profiling and eliminating bottlenecks across the foundation model training stack stack, from data pipelines to GPU kernels

Design, build, and optimize distributed training systems (PyTorch) for multi-node GPU clusters, ensuring scalability, robustness, and high utilization

Implement efficient low-level code (CUDA, cuDNN, Triton, custom kernels) and integrate it seamlessly into high-level training frameworks

Optimize workloads for hardware efficiency: CPU/GPU compute balance, memory management, data throughput, and networking

Develop monitoring and debugging tools for large-scale runs, enabling rapid diagnosis of performance regressions and failures

### What You’ll Bring

Deep experience in distributed systems, ML infrastructure, or high-performance computing (8+ years)

Production-grade expertise in Python

Low-level performance mastery: CUDA/cuDNN/Triton, CPU–GPU interactions, data movement, and kernel optimization

Scaling at the frontier: experience with PyTorch and training jobs using data, context, pipeline, and model parallelism

System-level mindset with a track record of tuning hardware–software interactions for maximum utilization

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Agenesis%3A2196f682-bd01-4f83-992d-367bbb3c8e8b and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.