Senior Research Engineer - Safety Tooling and Data

Role Overview

As a Senior Research Engineer in our Safety team, you will play a key role in helping develop safer, more secure, and more reliable models. Your primary focus will be on building tools to enable easy data synthesis, analysis, and management, for complex combinations of real and synthetic data that is used in both model training and evaluation. You will own the cohesive vision of these tooling repositories.

You will work closely with a team of research scientists and engineers to create tooling that enables tighter experimentation cycles, better data coverage of the real world, and more scientific rigour. You will have a lot of autonomy and need to be opinionated about what areas of the codebase need elegance and standards, and where that would be overengineering. You will be given high level experimental problems that need to be solved with efficient pipelines, and design and implement the solutions. Your data analysis will collaboratively feed into modelling decisions and experimentation.

This role combines expertise in software engineering, statistics, and data science. You will be working on the Modelling Safety and Trust team, so interest in these areas is a plus, but is not at all required.

*Timezone restrictions apply (UK, Europe, or ET), but there are no location restrictions.*

Key Responsibilities

• Design and implement robust data pipeline tooling that enables frequent, low-friction data generation and annotation.
• Create cohesive data infrastructure that supports continuous parallel operation with model experimentation.
• Establish standardized processes for data validation, analysis, and improvement of data both training and evaluation.
• Collaborate with the ML modeling team to align data capabilities with experimental needs.
• Maintain opinionated, well-documented solutions that become team standards.
• Develop systematic analysis frameworks to identify incoming data sources and benchmark coverage gaps.

Qualifications

• Extremely strong software engineering skills.
• Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance.
• Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch) and Big Data Analytics (e.g., BigQuery, SQL).
• Demonstrated ability to own complex technical projects from conception to deployment.
• Opinionated approach to technical architecture with ability to make principled decisions.
• Understanding of ML data requirements and the intersection of data engineering with modeling workflows.