Applied Research Engineer, LLM Alignment (Remote)

Poolside; United Kingdom; On-site; GBP 110,000 - 160,000; Full time

Benefits offered by this job

Fully remote work

37 days vacation & holidays

Health insurance allowance for you &/+

16 weeks of parental leave

Well-being & home office allowances

Company-provided equipment

Frequent team get togethers

Diverse & inclusive culture

Job summary

Poolside is seeking a highly skilled Applied Research team member to turn pre-trained LLMs into well-aligned, capable AI systems for coding and software development. You will work across data pipelines, post-training algorithms, and experimental design, with access to thousands of GPUs to accelerate research.

The role blends research and engineering, requiring deep learning expertise, Python, PyTorch or Jax, and a hands-on approach in a fast-moving environment with cross-functional collaboration.

Qualifications

• Experience with Large Language Models (LLM) and Transformers.
• Strong background in deep learning and model alignment research.
• Proven ability to design and evaluate training/fine-tuning experiments.
• Experience building data pipelines and experimentation environments.

Responsibilities

• Research and experiment on specializing foundational models for agentic use cases.
• Build and maintain data and training pipelines.
• Stay current with state-of-the-art LLMs, alignment, and code generation research.
• Design, analyze, and iterate on training/fine-tuning experiments.
• Write clean, pragmatic code and collaborate with peers.

Skills

LLMs

Transformers

Deep learning

Data pipelines

Post-training

Distributed training

Research

Python

PyTorch

Jax

Tools

PyTorch

Jax

Docker

Job description

Poolside is seeking a highly skilled Applied Research team member to turn pre-trained LLMs into well-aligned, capable AI systems for coding and software development. You will work across data pipelines, post-training algorithms, and experimental design, with access to thousands of GPUs to accelerate research.

The role blends research and engineering, requiring deep learning expertise, Python, PyTorch or Jax, and a hands-on approach in a fast-moving environment with cross-functional collaboration.