Senior Research Engineer - Safety Tooling and Data

About the role

This role centers on enhancing AI model safety by developing sophisticated tooling for data generation, annotation, and analysis. The engineer will design and implement data pipeline tools that facilitate frequent data operations and create infrastructure to support parallel model experimentation. This work is critical for improving both training and evaluation data within Cohere's AI systems.

A key part of this position involves establishing standardized processes for data validation and improvement, ensuring data quality for both training and evaluation. The Senior Research Engineer will also collaborate closely with the ML modeling team to ensure data capabilities align with experimental needs and to develop frameworks for identifying data sources and benchmarking coverage gaps.

Success in this role means owning complex technical projects from start to finish, from initial concept to deployment. The ideal candidate will bring a strong, opinionated approach to technical architecture, making principled decisions that lead to well-documented solutions that become team standards. This contributes directly to Cohere's mission of building cutting-edge foundation AI models for enterprise clients.

The salary for this role ranges from $230,000 to $380,000 USD annually.

Skills that matter here

• Python: This role requires proficiency in Python for software engineering tasks and developing data pipeline tools.
• PyTorch: Experience with ML frameworks like PyTorch is necessary for understanding model experimentation and data requirements.
• BigQuery: This position involves using BigQuery for big data analytics within the data infrastructure.
• SQL: SQL proficiency is needed for managing and querying data within the established data infrastructure.

Who this role suits

• A person who thrives on owning technical projects from beginning to end.
• Someone who has a strong perspective on technical architecture and can make well-reasoned decisions.
• An individual with a deep understanding of machine learning data requirements and how data engineering intersects with modeling.
• A candidate who values creating well-documented, standardized solutions for team use.

From the employer

• 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. 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

• A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
• Full health and dental benefits, including a separate budget for mental health.
• RRSP matching, 401K, Pension Scheme.
• 100% Parental Leave top-up for up to 6 months, for either parent.
• Annual enrichment benefits:
• Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
• Education & learning stipend for conferences, courses, and coaching.
• 6 weeks of paid vacation (30 working days!)
• Budget for traveling to other offices if you are remote, plus an annual company offsite.