AI Safety Lab Lead (RCO)

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At the NRC youll also discover new possibilities. Our supportive workplace fosters a culture of creativity welcoming fresh perspectives and innovation at all levels. We value teamwork. Youll collaborate across multiple fields and with the brightest minds to find creative solutions. Most importantly youll discover whats possible within you as you grow make valuable contributions and progress in your professional journey. From ground-breaking discoveries to a life-changing career discover your possible at the NRC.

Are you passionate about safe and responsible AI Do you want to be part of a pioneering team shaping how AI systems are evaluated measured and deployed safely Do you want your work to have a real impact for Canada and Canadians If so we want to meet you!

We are seeking a midcareer RCO04 to establish and lead the NRCs AI Safety Lab a new capability funded by the Canadian AI Safety Institute (CAISI) and dedicated to evaluating the safety of advanced AI systems. You will be part of a pioneering team conducting rigorous AI safety evaluations and translating technical results into actionable tools guidance and recommendations for AI practitioners and policymakers across government and beyond.

Under the guidance of the Principal Advisor for AI Safety and as part of the Chief Digital Research Officers team you will play a central role in building and operating a scalable AI safety evaluation collaboration with leading experts from our Digital Technologies Research Centre you will design execute and monitor advanced evaluations at scale on frontier AI models and contribute to cuttingedge AI safety research carried out by the NRC in collaboration with CAISI partners nationally and internationally.

Key Responsibilities include:

• Establish and manage a multidisciplinary team of AI safety specialists forming the lab.
• Build and leverage state-of-the-art model evaluation infrastructure and frameworks.
• Work with AI safety researchers from the NRC and CAISI partners in designing running and documenting AI safety evaluations.
• Establish and steward standardized reproducible safety evaluation protocols.
• Develop and curate new AI safety benchmarks.
• Translate technical findings into practical solutions and advice such as new tools for evaluators/developers or recommendations to policy makers.

The ideal candidate would have:

• A track record of building and managing AI teams.
• Hands-on experience evaluating frontier AI models using safety evaluation frameworks (e.g. Inspect Moonshot).
• A strong background in compute cluster operations and scalable ML infrastructure.
• Experience designing reproducible evaluation protocols and benchmarks.
• Experience translating research into practical tools guidance or policy recommendations.
• The ability to collaborate across research government and international partners.
• Strong analytical judgment and initiative.
• Clear communication skills across technical and non-technical audiences.

Applicants must demonstrate within the content of their application that they meet the following screening criteria in order to be given further consideration as candidates:

Masters degree from a recognized university in Statistics Data Science Computer Science or Engineering or closely related to the position.

Equivalency

In lieu of a master degree a combination of a Bachelors degree with at least 2 years of directly relevant professional experience in applied AI or AI evaluation may be considered.

For information on certificates and diplomas issued abroad please see Degree equivalency

You must meet the essential experience criteria and at least two of the Specialised Experience to move forward in this process.

Essential Experience

• Recent* experience leading or managing technical teams in an AI/ML data analytics or digital technologies environment.
• Recent* experience evaluating benchmarking or red-teaming AI models (open-source or closed-source) or AI systems.
• Experience in managing AI/ML or data analytics projects for government or industry clients.
• Experience developing Python-based tooling and automation for ML/AI workflows.
• Experience in documenting experimental AI results through technical reports internal documentation or scientific publications.

*Recent is defined as within the last 3 years.

Specialised Experience

• Experience with red-teaming workflows adversarial testing or stress-testing of AI systems (including agentic systems).
• Experience with compute clusters and job orchestration (e.g. SLURM).
• Experience with ML experiment tracking tools (e.g. MLflow Weights & Biases).
• Experience developing or curating AI benchmark datasets and associated documentation (e.g. through HuggingFace).
• Experience translating AI research into practical tools guidance or policy recommendations.
• Experience conducting AI projects with researchers or through international collaboration.

Reliability Status

For a Reliability Status verification of background information over a period of 5 years is required.

BilingualImperativeCBC/CBC

Information on language requirements and self-assessment tests

Candidates will be assessed on the basis of the following criteria:

• Ability to lead the development of reproducible evaluation protocols and benchmarks for frontier AI models and systems.
• Ability to communicate complex technical findings clearly to researchers government stakeholders and policy audiences.
• Knowledge of AI safety concepts risk taxonomies or governance frameworks (e.g. TBS guidelines NIST AI Risk Management Framework) and their connection to technical evaluation.
• Ability to design and oversee scalable AI evaluation infrastructure across on-premises and cloud environments.
• Knowledge of AI safety evaluation frameworks (e.g. Inspect AI Moonshot) and their application to frontier model assessment.
• Ability to make sound technical decisions when configuring evaluations across different model types balancing compute budgets reproducibility and result validity.
• Ability to manage and prioritize competing technical workstreams across a multidisciplinary team.
• Knowledge of agentic AI architectures and how design choices such as tool access memory and planning affect safety-relevant behaviours.
• Ability to design experiment tracking and data management workflows that support reproducibility across multiple researchers and evaluation campaigns.

For this position the NRC will evaluate candidates using the following competency profile(s):Management Services; Research;Supervisor

View all competency profiles

This position is classified as a Research Council Officer (RCO) a group that is unique to the NRC. The intent of this hiring action is to staff this position at the RCO-3 to RCO-4level which is a mid-career level position with a salary range of $119688to $168192.

NOTE: Please note that the full RO/RCO salary scale has five levels. Salary determination will be based on a review of the candidates expertise outcomes and impacts of their previous work experience relative to the requirements of the such the initial salary could be within another level of the RO/RCO salary scale (i.e. above or below the intended level for this position).

In addition the incumbent will receive the Bilingualism Bonus of $800 per year.

NRC employees enjoy a wide-range of competitive benefits including a robust pension plan comprehensive health and dental coverage disability and life insurance office closure at the end of December and additional supports to enhance your well-being throughout your career and beyond.

• Relocation assistance will be determined in accordance with the NRCs directives.
• A pre-qualified list may be established for similar positions for a one year period.
• Preference will be given to Canadian citizens and permanent residents of Canada. Please include citizenship information in your application.
• The incumbent must adhere to safe workplace practices at all times.
• We thank all those who apply however only those selected for further consideration will be contacted.