AI Research Engineer Job Description

What "AI Research Engineer" means

The AI Research Engineer role sits at the boundary between research and engineering. Unlike AI Engineers who build products with existing models, Research Engineers advance the capabilities of the models themselves — working on training methodology, model architecture, evaluation frameworks, and novel applications of emerging techniques.

Across 23 active listings, the most striking pattern is the stack: Python (89%), PyTorch (78%), and JAX (72%) dominate — reflecting a fundamentally different technical world from the LangChain / RAG stack that characterises most AI engineering roles. This role requires deep ML fundamentals, often a postgraduate background, and comfort with reading and implementing research papers. The 78% remote rate is the highest of any AI engineering category — research roles have always been globally distributed.

Required skills

Frequency = % of 23 active listings that mention this skill. Priority is derived from frequency and listing emphasis.

Day-to-day responsibilities

• 1Read and implement recent ML research papers; evaluate whether new techniques are relevant to the company's model development
• 2Run large-scale training experiments on GPU clusters; analyse results and iterate on training methodology
• 3Develop and improve model evaluation frameworks — benchmarks, human evaluation, automated scoring
• 4Contribute to pre-training, fine-tuning, or post-training (RLHF, DPO, Constitutional AI) of language models
• 5Collaborate with infrastructure engineers on compute efficiency — reducing training cost per FLOP
• 6Write technical reports and research memos; sometimes publish externally
• 7Mentor ML Engineers and contribute to the team's collective understanding of model capabilities

How it differs from related roles