Senior Research Engineer - Safety Tooling and Data

About the role

This role focuses on developing robust tooling and infrastructure to enhance the safety of AI models. The Senior Research Engineer will design and implement data pipelines for efficient data generation and annotation, ensuring these systems support continuous model experimentation. This work involves creating standardized processes for data validation and analysis to improve both training and evaluation data.

A key aspect of this position is collaboration with the ML modeling team, aligning data capabilities with their experimental needs. The engineer will also be responsible for maintaining well-documented solutions that set team standards and developing systematic analysis frameworks to identify data sources and benchmark coverage gaps. This ensures a cohesive and integrated approach to data management within the AI development lifecycle.

Success in this role means owning complex technical projects from conception to deployment, demonstrating strong software engineering and statistical skills. The ideal candidate will have an opinionated approach to technical architecture, making principled decisions, and a deep understanding of how data engineering intersects with machine learning modeling workflows to deliver safer, more effective AI solutions.

The salary for this full-time senior remote role ranges from $230,000 to $380,000 USD.

Skills that matter here

• Python: Proficiency in Python is required for implementing robust data pipeline tooling and analysis frameworks.
• PyTorch: Experience with ML frameworks like PyTorch is necessary for aligning data capabilities with model experimentation.
• BigQuery: BigQuery skills are used for big data analytics, supporting the creation of cohesive data infrastructure.
• SQL: SQL proficiency is essential for managing and analyzing large datasets within the data infrastructure.
• software engineering: Extremely strong software engineering skills are fundamental for designing and implementing robust, maintainable solutions.
• statistical skills: Strong statistical skills are needed for evaluating scientific experiments related to data collection and model performance.

Who this role suits

• Someone who enjoys taking ownership of complex technical projects from start to finish.
• An individual who values standardized processes and well-documented solutions.
• A person with a strong opinion on technical architecture and the ability to make principled decisions.
• A candidate who understands the intersection of data engineering and machine learning workflows.

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.