Senior Research Engineer - Safety Tooling and Data

Overview

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build. Each one of us contributes to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more who are passionate about their craft. We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris.

Join us!

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 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 over engineering. 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. If any of these topics sound interesting to you, we encourage you to apply.

You will be working on the Modeling Safety and Trust team, so interest in these areas is a plus, but is not at all required.

Please Note: We have offices in London, Edinburgh, Paris, Toronto, Montreal and New York, but we also embrace being remote-friendly. This role has timezone restrictions (UK, Europe, or ET) but no location restrictions.

• 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

• 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. Big Query, 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

This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.

Cohere is committed to fair and transparent pay practices. The salary range listed for this role reflects the expected base compensation. Actual compensation offered will be determined by factors such as location, level,…