Lead policy advocacy for pragmatic AI safety legislation at the state level, including legislative analysis, drafting, and engagement with policymakers and AI companies.
Curated roles
Browse remote-friendly roles across AI safety, governance, policy, evaluations, compliance and responsible AI teams.
190 active roles found.
Lead policy advocacy for pragmatic AI safety legislation at the state level, including legislative analysis, drafting, and engagement with policymakers and AI companies.
Lead recruitment and implementation support for state AI regulatory roles, with responsibilities spanning AI safety legislation, compliance assessment, and enforcement communications.
Project-based adversarial evaluation and red teaming network for frontier AI systems, with work spanning ML security, prompt injection/jailbreaking, agentic system evaluation, and translating findings into governance and mitigation decisions.
Policy Director role supporting executive operations while building AI policy expertise at a nonprofit focused on pragmatic policies to reduce severe risks from frontier AI models.
Funding call for foundational research on safety and risk in multi-agent AI systems, including emergent dynamics, trustworthy interaction infrastructure, and scalable monitoring and control.
Open call for contractors to support CLTR’s AI policy unit and related AI-bio work, including AI governance, AI safety concepts, frontier-lab security practices, and safeguard approaches for capable models.
State policy and public affairs role focused on OpenAI’s AI policy priorities, including guardrails for advanced AI models, legislative review, and engagement with state and local governments.
EU policy role focused on shaping OpenAI’s approach to AI-related legislative and regulatory issues in Brussels, including engagement with policymakers and support for regulation and industry standards.
Research engineer role focused on building and maintaining AI safety evaluation benchmarks, guardrails, and research on agentic failure modes.
Senior ML engineer role building product experiences, evaluation systems, and trustworthy language-model workflows for research and high-stakes decision-making; relevant because it explicitly emphasizes careful evaluations, process supervision, and safer AI systems.
Remote researcher role producing public reviews of AI benchmarks, evaluating methodologies and implications for AI capabilities; adjacent to AI safety via model evaluations and capability assessment.
Senior technical role on Cohere’s Safety for Agents team focused on data generation, post-training algorithms, and evaluation methods to improve safety, trustworthiness, and security of LLMs and agentic models.
Research scientist role at Resolution focused on technical AI alignment research, including empirical and theoretical work on scalable oversight and related alignment problems.
Research Engineer role at an ASI alignment lab, building research automation and evaluation infrastructure to support alignment research and frontier-model experiments.
Senior research engineer role on Cohere’s Safety / Modelling Safety and Trust team building data tooling and pipelines for training and evaluation data to support safer, more reliable models.
Research role focused on evaluating frontier AI models on real-world tasks, building benchmarks and rubrics, and analyzing model performance; directly relevant to AI evaluations and safety-adjacent capability assessment.
Research engineer role building AI verification prototypes and supporting technical communications for a verification team whose work is explicitly tied to policy change, international agreements, and trustworthy AI adoption.
Lead research on frontier AI risk modeling across cyber, CBRN, and loss of control, setting methodological standards that inform safety cases, evaluations, mitigations, and AI governance.
Senior research role focused on creating next-generation evaluation methods, benchmarks, and infrastructure to measure LLM progress and model capabilities.
Forward Deployed Engineer role building and deploying LLM-powered agentic workflows for enterprise customers, with explicit responsibility for evaluation frameworks, safety, observability, and auditability of AI systems.