Application Guide
How to Apply for Safety Research Grants
at Thinking Machines
🏢 About Thinking Machines
Thinking Machines is an AI startup founded by Miri Murati, known for her leadership at OpenAI and her focus on building safe, open-weight AI systems. The company stands out by offering public credits for external safety research, signaling a commitment to transparency and collaborative risk mitigation. Working here means shaping the frontier of AI safety alongside a team that values both technical rigor and open scientific inquiry.
About This Role
This is a grant-funded research opportunity, not a traditional employment role: you propose and execute safety research on Thinking Machines' open-weight models, with up to $50,000 in compute credits. You will investigate alignment failure modes from fine-tuning, reward hacking, oversight gaming, and adversarial robustness, with the goal of producing actionable insights that inform model development and deployment. Your findings could directly influence how Thinking Machines designs tamper-resistant training and defensive capabilities.
💡 A Day in the Life
A typical day involves running adversarial fine-tuning experiments on Thinking Machines' open-weight models, analyzing results for signs of safeguard degradation, and documenting findings for a public report. You might also spend time refining your experimental design, consulting with the Thinking Machines team on infrastructure, and preparing your next round of compute-intensive tests. The work is self-directed but highly collaborative, with a focus on producing actionable safety insights.
🚀 Application Tools
🎯 Who Thinking Machines Is Looking For
- Has hands-on experience with adversarial fine-tuning, red-teaming, or safety evaluations of large language models, ideally on open-weight models.
- Can design experiments to distinguish between genuine safeguard persistence and mere suppression, and is comfortable quantifying worst-case and marginal risk.
- Is familiar with reward hacking, oversight gaming, and alignment failure modes from fine-tuning, and can forecast safety-relevant scaling trends.
- Has a track record of publishing or sharing safety research openly, and can work independently in a remote, grant-supported capacity.
📝 Tips for Applying to Thinking Machines
Frame your proposal around one or more of the listed directions (differential defensive capabilities, hazardous data filtering, tamper-resistant training) and explicitly connect it to Thinking Machines' open-weight models.
Quantify your expected credit usage: break down how you would allocate up to $50,000 across experiments, and justify why that budget is sufficient to answer your research questions.
Demonstrate familiarity with Thinking Machines' specific models and any public safety documentation; reference them by name in your application to show you've done your homework.
Propose a concrete deliverable, such as a public report, code repository, or benchmark, that would be useful to both the company and the broader safety community.
Highlight any prior experience with adversarial fine-tuning or reward hacking research, and include links to published work or GitHub repos that showcase your methods.
✉️ What to Emphasize in Your Cover Letter
["Your specific research plan: what safety question you will investigate, why it matters for open-weight models, and how it maps to Thinking Machines' stated directions.", 'Your technical approach: the methods you will use (e.g., adversarial fine-tuning, red-teaming, data filtering) and how you will measure success.', 'Your ability to execute independently: evidence of past self-directed research, grant management, or remote collaboration.', "Your alignment with Thinking Machines' mission: why open-weight safety research is important to you and how your work would contribute to the company's goals."]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read Thinking Machines' public blog posts, model cards, and any safety documentation to understand their current safety philosophy and open-weight model releases.
- → Review Miri Murati's public talks and interviews about AI safety and open-weight models to align your proposal with her vision.
- → Study recent papers on adversarial fine-tuning, reward hacking, and tamper-resistant training to identify gaps that your research could fill.
- → Investigate the compute credit allocation process: understand how $50,000 translates to GPU hours on Thinking Machines' infrastructure and plan your experiments accordingly.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Proposing a generic safety research idea that could apply to any AI company, rather than tailoring it to Thinking Machines' open-weight models and specific directions.
- Failing to justify the budget: not explaining how you would use $50,000 in credits or why that amount is necessary for your experiments.
- Neglecting to mention how your findings would be shared or used to improve safety, which is central to this grant's purpose.
📅 Application Timeline
⏰ Deadline: September 25, 2026
We recommend applying at least a few days early to avoid last-minute technical issues.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!
Ready to Apply?
Good luck with your application to Thinking Machines!