{"id":529706,"date":"2026-04-14T04:05:22","date_gmt":"2026-04-14T04:05:22","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/529706\/"},"modified":"2026-04-14T04:05:22","modified_gmt":"2026-04-14T04:05:22","slug":"applications-are-open-for-the-anthropic-fellows-program-apply-by-26-april-2026","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/529706\/","title":{"rendered":"Applications are Open for the Anthropic Fellows Program -Apply By 26 April 2026"},"content":{"rendered":"

The Anthropic Fellows Program <\/a>is a structured research initiative created by Anthropic to develop emerging talent in AI research, engineering, and AI safety. It is designed to support individuals with strong technical potential, regardless of formal experience, and to provide them with the resources, mentorship, and funding required to conduct impactful empirical research in alignment with frontier AI safety priorities.<\/p>\n

Anthropic is a public benefit corporation focused on building reliable, interpretable, and steerable AI systems that are safe and beneficial for society. The organization brings together researchers, engineers, policy experts, and operational leaders to advance the long-term development of safe artificial intelligence systems.<\/p>\n

Program Structure and Objectives<\/p>\n

The Fellows Program is designed as an intensive, full-time research experience aimed at producing high-quality public outputs, such as research papers or technical reports. Fellows are expected to work on empirical projects using external infrastructure such as open-source models and public APIs.<\/p>\n

Key objectives of the program include:<\/p>\n

Advancing AI safety and security research through hands-on empirical work<\/p>\n

Producing publishable research outputs (many past fellows have published papers)<\/p>\n

Building talent pipelines into frontier AI research roles<\/p>\n

Supporting interdisciplinary collaboration across AI-related fields<\/p>\n

Encouraging exploration of scalable, real-world AI safety problems<\/p>\n

In previous cohorts, more than 80% of fellows successfully produced research publications, demonstrating the program\u2019s emphasis on execution and scientific contribution.<\/p>\n

Program Duration, Compensation, and Logistics<\/p>\n

The program runs for approximately four months on a full-time basis. Fellows are expected to work around 40 hours per week, with the possibility of extension based on performance and project needs.<\/p>\n

Participants receive structured support, including:<\/p>\n

Weekly stipend:<\/p>\n

3,850 USD<\/p>\n

2,310 GBP<\/p>\n

4,300 CAD<\/p>\n

Research funding for compute and infrastructure (~$15,000\/month)<\/p>\n

Access to shared workspace locations in:<\/p>\n

Berkeley, California<\/p>\n

London, United Kingdom<\/p>\n

Optional remote participation for eligible candidates in the US, UK, or Canada<\/p>\n

Access to mentorship from leading researchers at Anthropic<\/p>\n

Integration into a broader AI safety and research community<\/p>\n

Visa sponsorship is not provided for fellows, and participants must already have work authorization in the US, UK, or Canada.<\/p>\n

Application Timeline and Process<\/p>\n

Applications are reviewed on a rolling basis, with structured cohorts beginning periodically. The next cohort begins on July 20, 2026, and applications must be submitted by April 26, 2026 for consideration.<\/p>\n

The interview and selection process typically includes:<\/p>\n

Initial application screening<\/p>\n

Reference checks<\/p>\n

Technical assessments and interviews<\/p>\n

Research-focused discussions with potential mentors<\/p>\n

Applicants are encouraged to apply even if they do not meet every listed qualification, as the program values potential, motivation, and research curiosity over rigid credential requirements.<\/p>\n

Fellowship Workstreams<\/p>\n

The program is organized into multiple specialized workstreams. Applicants may be considered across all areas based on their skills and preferences.<\/p>\n

AI Safety Fellows<\/p>\n

This workstream focuses on reducing catastrophic risks from advanced AI systems.<\/p>\n

Research areas include:<\/p>\n

Scalable oversight of advanced models<\/p>\n

Adversarial robustness and AI control<\/p>\n

Mechanistic interpretability of model internals<\/p>\n

Model organisms of misalignment<\/p>\n

AI welfare and evaluation frameworks<\/p>\n

Typical profiles include candidates with:<\/p>\n

Experience in empirical machine learning research<\/p>\n

Strong Python programming ability<\/p>\n

Interest in interpretability or alignment problems<\/p>\n

Contributions to open-source AI research<\/p>\n

AI Security Fellows<\/p>\n

This track focuses on identifying and mitigating security vulnerabilities in AI systems.<\/p>\n

Key focus areas:<\/p>\n

Offensive security and vulnerability research<\/p>\n

Red teaming and adversarial testing<\/p>\n

LLM security and safety evaluation<\/p>\n

Bug bounty experience and CVE reporting<\/p>\n

Ideal candidates often demonstrate:<\/p>\n

Experience in cybersecurity or penetration testing<\/p>\n

Strong open-source contributions in ML or security domains<\/p>\n

Ability to solve ambiguous technical problems independently<\/p>\n

ML Systems & Performance Fellows<\/p>\n

This stream emphasizes infrastructure, scalability, and systems-level ML engineering.<\/p>\n

Work may include:<\/p>\n

Building high-performance ML systems<\/p>\n

Developing simulation environments for AI workloads<\/p>\n

Optimizing training and inference pipelines<\/p>\n

Supporting infrastructure-heavy research projects<\/p>\n

Strong candidates typically have:<\/p>\n

Experience with distributed systems and ML infrastructure<\/p>\n

Engineering expertise in large-scale computing systems<\/p>\n

Ability to balance research and production-grade engineering<\/p>\n

Reinforcement Learning Fellows<\/p>\n

This track focuses on reinforcement learning research and applied experimentation.<\/p>\n

Key areas include:<\/p>\n

RL environments for model training<\/p>\n

Generalization studies in reinforcement learning<\/p>\n

Model-based tools for training data analysis<\/p>\n

Algorithm development and experimentation<\/p>\n

Ideal profiles include:<\/p>\n

Strong ML systems engineering experience<\/p>\n

Familiarity with training and fine-tuning models<\/p>\n

Ability to debug complex model training processes<\/p>\n

Economics & Societal Impacts Fellows<\/p>\n

This workstream explores the broader societal implications of AI systems.<\/p>\n

Research topics include:<\/p>\n

Economic impacts of AI adoption<\/p>\n

Labor market and workforce transformation studies<\/p>\n

Human-AI collaboration analysis<\/p>\n

Model evaluation for societal well-being<\/p>\n

Policy-relevant empirical research<\/p>\n

Strong candidates often have:<\/p>\n

Background in economics, social science, or data analysis<\/p>\n

Strong writing and communication skills<\/p>\n

Ability to interpret ambiguous empirical results<\/p>\n

Interest in AI policy and societal impact<\/p>\n

Candidate Profile and Requirements<\/p>\n

Across all workstreams, ideal candidates typically demonstrate:<\/p>\n

Strong motivation to improve AI safety and societal outcomes<\/p>\n

Technical proficiency in Python programming<\/p>\n

Ability to work full-time during the program duration<\/p>\n

Experience in computer science, mathematics, physics, or related disciplines<\/p>\n

Comfort working in fast-paced collaborative research environments<\/p>\n

Strong communication and execution skills<\/p>\n

Additional advantages include:<\/p>\n

Open-source contributions<\/p>\n

Prior ML or systems research experience<\/p>\n

Domain expertise relevant to a chosen workstream<\/p>\n

Program Philosophy and Values<\/p>\n

Anthropic\u2019s research approach is grounded in the belief that AI safety is a critical global challenge requiring large-scale, collaborative scientific effort. The organization treats AI research as an empirical science, similar in rigor and methodology to physics or biology.<\/p>\n

Core values include:<\/p>\n

Focus on long-term AI safety and alignment<\/p>\n

Emphasis on interpretability and steerability of AI systems<\/p>\n

Collaborative, cross-disciplinary research culture<\/p>\n

Commitment to transparency and public research dissemination<\/p>\n

Inclusion of diverse perspectives in shaping AI futures<\/p>\n

For more opportunities such as these please follow us on\u00a0Facebook<\/a>,\u00a0Instagram<\/a>,\u00a0WhatsApp<\/a>,\u00a0Twitter<\/a>,\u00a0LinkedIn<\/a>\u00a0and\u00a0WPChannel<\/a><\/p>\n

Disclaimer:\u00a0Global South Opportunities (GSO)<\/a>\u00a0is not the organization offering the program. For any inquiries, please contact the official organization directly. Please do not send your applications & CVs to GSO, as we are unable to process them. Due to the high volume of emails, we receive daily, we may not be able to respond to all inquiries. Thank you for your understanding.<\/p>\n

\n\tRelated\n<\/p>\n

\t\t