AI Research Engineer - AI Safety Platform

Harnham; Remote; GBP 170,000 - 200,000; Full time

Job summary

A Europe-based deep learning company is looking for an AI Engineer to build frontier-level LLM models. This hands-on role focuses on training and optimizing large models using distributed frameworks, along with developing multimodal training pipelines. The position is 100% remote, offering a salary of up to £200k plus equity. Candidates should have experience with GPU training, CUDA, and frameworks like PyTorch and DeepSpeed.

Qualifications

• Experience with large-scale GPU training and models.
• Hands-on experience in LLM and VLM training.
• Strong knowledge of CUDA/Triton and optimizing distributed systems.

Responsibilities

• Train LLMs and VLMs from scratch using distributed frameworks.
• Build and optimize multimodal training pipelines.
• Develop and refine Mixture-of-Experts architectures.

Skills

GPU training

CUDA

DeepSpeed

PyTorch

Triton

Mixture-of-Experts

multimodal pipelines

Job description

Do you want to build frontier-level LLM models from scratch?

Have you worked on large-scale GPU training, Triton/CUDA, or MoE systems?

Are you ready to join one of Europe’s most technical deep-learning teams?

A Europe-based deep learning company is building the next generation of foundation models. Think of a smaller, faster, highly technical version of the major frontier labs – focused on LLM/VLM training, GPU efficiency, safety layers, and advanced architectures. They are preparing for their next funding milestone and operate with an extremely high technical bar.

They are hiring an AI Engineer to focus on training, scaling, and optimising large models. This role is hands-on, research-driven, and sits at the core of model creation. The AI Engineer will train LLMs and VLMs from scratch, optimise distributed GPU systems, and contribute to new architectures including Mixture-of-Experts and multimodal pipelines. You’ll work closely with a small team of world-class engineers on one of the most technical problems in AI.

Key responsibilities

• Train LLMs/VLMs from scratch using distributed frameworks
• Build and optimise multimodal training pipelines (text, image, audio)
• Develop and refine Mixture-of-Experts architectures
• Write and optimise CUDA/Triton kernels
• Improve training stability, speed, and memory efficiency
• Experiment with new architectures, scaling laws, and data mixtures

Key details

• Salary: Up to £200k + equity (0.1–0.3%)
• Working model: UK, 100% remote
• Stack: PyTorch, Megatron, DeepSpeed, Triton/CUDA, multimodal architectures

Interested? Please apply below.