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80,000 Hours technical AI safety upskilling resources
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3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: 80,000 Hours
Published by 80,000 Hours in June 2025, this resource is aimed at career-changers and students seeking structured pathways into technical AI safety research; useful as a starting point for newcomers to the field.
Metadata
Importance: 55/100blog posteducational
Summary
A curated guide from 80,000 Hours providing resources for individuals looking to develop technical skills relevant to AI safety research. It aggregates learning materials, courses, and pathways to help people transition into or advance within the technical AI safety field. The resource supports field-building by lowering barriers to entry for aspiring AI safety researchers.
Key Points
- •Curates technical learning resources specifically aimed at building AI safety research skills
- •Targets individuals seeking to transition into or upskill within technical AI safety roles
- •Likely includes programming, ML, and alignment-specific learning pathways
- •Part of 80,000 Hours' broader career guidance mission for high-impact work
- •Supports the AI safety talent pipeline by making skill development more accessible
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| 80,000 Hours | Organization | 45.0 |
| AI Safety Field Building and Community | Crux | 0.0 |
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Want to upskill in technical AI safety? Here are 67 useful resources | 80,000 Hours Search for: Our new book, a ridiculously in-depth guide to a fulfilling career, is out May 2026. Preorder now
Are you enthusiastic about technical AI safety but need concrete ideas for how to enter the field?
Below are our top picks for upskilling in technical AI safety research, the field focused on ensuring powerful AI systems behave safely and as intended. In practice, upskilling involves developing the machine learning and research skills needed to work on challenges such as alignment and interpretability.
We developed this list in consultation with our advisors to highlight the resources they most commonly recommend, including articles, courses, organisations, and fellowships. While we recommend applying to speak to an advisor for tailored, one-on-one guidance, this page gives a practical, noncomprehensive snapshot of how you might move from being interested in technical AI safety to starting to work on it.
Overviews
These resources outline the technical AI safety landscape, highlighting current research efforts and some practical ways to begin contributing to the field.
AISafety.com
Shallow review of technical AI safety by technicalities et al.
AI safety technical research career guide - how to enter by 80,000 Hours
Levelling up in AI safety research engineering by Gabriel Mukobi
Recommendations for technical AI safety research agendas by Anthropic
Technical AI safety research areas by Coefficient Giving
An overview of areas of control work by Ryan Greenblatt, Redwood Research
AI safety needs great engineers by Andy Jones
AI safety courses
These courses can help you gain technical knowledge and practical research experience in AI safety.
ARENA’s curriculum
BlueDot Impact’s AI Alignment course
Andrej Karpathy’s Zero to Hero course His YouTube videos can also be great intro-friendly resources, as can 3Blue1Brown’s deep learning videos .
Deep Learning Curriculum by Jacob Hilton
Google ML Course
Ideas for projects and upskilling
If you’re looking for concrete ways to contribute to technical AI safety research, check out these resources:
What are some projects I can try? (AISafety.Info)
100+ concrete projects and open problems in evals by Marius Hobbhahn
A list of 45+ mech interp Projects by Apollo Research
Open problems in mechanistic interpretability by Sharkey et al.
Consider joining an alignment hackathon such as an Apart Research Sprint .
Consider joining Eleuther’s community of researchers on their Discord.
Consider writing a task using the METR framework .
Consider writing your research theory of change ( workshop slides , Michael Aird).
Advice from technical AI safety researchers
Many experts in the field have practical tips for getting involved in technical AI safety work. Here is some of our favourite advice:
Karpathy on PhDs, research agendas, career advice
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