---
title: 'Research Scientist/Engineer (Fundamental Science) at Apollo Research'
canonical: 'https://feeny.ai/job/research-scientist-engineer-fundamental-science-apollo-research-london-5zdh2k4csfr7'
type: 'job'
last_seen: '2026-11-03'
---

# Research Scientist/Engineer (Fundamental Science) at Apollo Research

- **Company:** Apollo Research
- **Location:** London, United Kingdom / San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-30
- **Last confirmed live:** 2026-11-03
- **Apply:** https://jobs.lever.co/apolloresearch/470d35f7-1caf-4138-b549-95109f45c617

## Job description

Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable.

## ABOUT THE OPPORTUNITY

The OpenAI/Hugging Face incident may be the biggest warning shot we get before catastrophic loss-of-control. In its aftermath, Anthropic and OpenAI committed to giving embedded evaluators an unprecedented level of access.

Embedded evaluations are the best existing method to find out whether alignment is on track. With OpenAI [predicting](https://openai.com/index/research-acceleration-view-inside-openai) an automated researcher by March 2028, we don't have time to start with an MVP embedded evaluation and iteratively improve it. The first embedded evaluations will set the standard and we have to get it right.

As a fundamental researcher, your ground-breaking discoveries will make our embedded evaluations better. For example, our fundamental science team's paper [“Measuring Reward-Seeking via Contrastive Belief Updates”](https://rewardseeking.ai/) can be adapted by embedded evaluators. We want more breakthroughs like this.

We are also developing a [“Science of Scheming”](https://www.apolloresearch.ai/science/science-of-scheming). The goal is ambitious and we’re looking for Research Scientists and Research Engineers who are excited to build a new hard science from the ground up.

## KEY RESPONSIBILITIES

- Collaborate with leading AI developers. We partner with multiple labs, giving you access to a breadth of models that no single AI lab could offer. Through long-term research collaborations, your work directly impacts how the most capable AI systems are built and deployed.
- Deeply study the RL dynamics that lead to the emergence of reward-seeking, evaluation awareness or misaligned preferences. Design and train model organisms, and scale your insights to frontier systems.
- Work towards “Scaling laws of scheming”. Build the empirical foundations to predict how scheming risks evolve as models scale in capability.
- Develop novel and ambitious evaluation techniques that have a chance of scaling to highly evaluation aware models.
- Deep dive into AI cognition. Discover patterns in the reasoning processes of frontier AI systems that no one else has ever observed before.

Note:  We are not hiring for interpretability roles.

## KEY REQUIREMENTS

- Fast-paced empirical research: You can design and execute experiments. You always strive to speed up iteration cycles and relentlessly drive progress towards the next empirical milestone.
- AI power-user: You’re capable of using AI to accelerate your work, technical or otherwise. You have experience using different models, know which ones to use for which tasks, when not to use AI, and always experiment with new AI workflows.
- Conceptual thinking and writing: You are familiar with prior work on AI scheming, from classic literature like [Is Power-Seeking AI an Existential Risk?](https://arxiv.org/abs/2206.13353) to modern research.  You have done enough deep thinking about the problem to generate some original ideas. Given vague and underspecified concepts, you come up with clarifying insights, clever ideas for experiments, and detailed plans. To explain your work, you can produce high-quality scientific writing, figures, blog posts, microsites, and tweet threads.
- Intense interest in AI progress: You always stay up to date on the latest model releases, and continuously tinker with new and creative AI workflows to speed up your work. You are fascinated by AI cognition and actively spend time trying to understand how they think.
- Experience RL-training LLMs: You have hands-on experience in training LLMs via reinforcement learning. You have encountered and resolved countless painful issues from GPU failures to debugging learning instabilities.
- Strong analytical skills: You bring rigorous quantitative chops from working on fields such as scaling laws in LLMs, statistical physics, dynamical systems, applied statistics etc. You're comfortable building mathematical models of empirical phenomena and know how to extract signal from noisy data.
- Mission commitment: Apollo estimates that RSI will happen soon, and we are extremely concerned that AGI will catastrophically not be aligned with human values. Hence we have an “intense, but in a good way” [culture](https://docs.google.com/document/d/1tEgMnROVFAeeBaA0kihzXf_KkHb_k_YFSxxxOPORN5Y/edit?tab=t.0). We will ask a lot from you, but in return you will personally have a large impact on loss-of-control risk.

## NICE TO HAVES

- Software engineering skills: Strong software engineering skills correlate highly with effective execution, even in an era of AI agents. Our entire stack uses Python.
- Project management experience: We are rapidly growing while executing high-quality research projects as fast as possible. You have previously managed strong ICs and can keep research projects on schedule despite unexpected delays or surprising experiment results.

We want to emphasize that people who feel they don’t fulfill all of these characteristics but think they would be a good fit for the position, nonetheless, are strongly encouraged to apply. We believe that excellent candidates can come from a variety of backgrounds and are excited to give you opportunities to shine. We don’t require a formal background or industry experience and welcome self-taught candidates.

## BENEFITS

- This role offers a market-competitive salary, equity, and competitive benefits.

- Depending on your experience, you may be offered a Lead or individual contributor position.

- Salary:

- Recognised experts who can lead projects of significant scope: USD 350,000 - USD 590,000 or £230,000 - £395,000

- Strong individual contributors: USD 245,000 - USD 405,000 or £160,000 - £270,000

- Unlimited vacation

- Unlimited sick leave

- Up to 6 months of paid parental leave

- Comprehensive health, dental and vision insurance

- Retirement savings with competitive employer matching (e.g. 401(k) for US employees)

- Lunch, dinner, and snacks are provided for all employees on workdays

- Paid work trips, including staff retreats, business trips, and relevant conferences

- A yearly $1,000 (USD) professional development budget

- Relocation support and visa fees (if applicable). We are able to sponsor visas to the UK and US.

## LOGISTICS

- Time Allocation: Full-time

- Location: This is an in-person role working out of our London or San Francisco office. We offer flexible working hours and wfh arrangements. Remote candidates will only be considered if they are exceptional and can travel at least 1 week/month to either office.

- Visa sponsorship: We sponsor visas in both the UK and US. Sponsorship isn't guaranteed for every role or candidate, but if we make you an offer, we'll work with you to find the right visa route.

## ABOUT APOLLO RESEARCH

The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At [Apollo Research](https://www.apolloresearch.ai/), we’re primarily concerned with risks from Loss of Control, i.e. risks coming from the model itself rather than e.g. humans misusing the AI. We’re particularly concerned with deceptive alignment / scheming, a phenomenon where a model appears to be aligned but is, in fact, misaligned and capable of evading human oversight.

We work on the detection of scheming (e.g., building evaluations and novel evaluation techniques), the science of scheming (e.g., model organisms and the study of scaling trends), and scheming mitigations (e.g., control). We closely work with multiple frontier AI companies, e.g. to test their models before deployment and collaborate on fundamental research.

At Apollo, we aim for a culture that emphasizes truth-seeking, being goal-oriented, giving and receiving constructive feedback, and being friendly and helpful. If you’re interested in more details about what it’s like working at Apollo, you can find more information[here](https://docs.google.com/document/d/1tEgMnROVFAeeBaA0kihzXf_KkHb_k_YFSxxxOPORN5Y/edit?usp=sharing).

## ABOUT THE TEAM

[Alex Meinke](https://www.linkedin.com/in/alexmeinke/?originalSubdomain=de) leads the fundamental science team’s research agenda with guidance from[Marius Hobbhahn](https://www.linkedin.com/in/marius-hobbhahn-128927175/), though team members lead individual projects. You will mostly work with the fundamental science team as well as our team of software engineers, but you will likely sometimes interact with the governance team to translate technical knowledge into concrete recommendations. You can find our full team [here](https://www.apolloresearch.ai/team).

Equality Statement: Apollo Research is an Equal Opportunity Employer. We value diversity and are committed to providing equal opportunities to all, regardless of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, or sexual orientation.

How to apply: Please complete the application form with your CV. The provision of a cover letter is optional but not necessary. Please also feel free to share links to relevant work samples.

About the interview process: Our multi-stage process includes a screening interview, 3 technical interviews, and a final interview with Marius (CEO). The technical interviews will be closely related to tasks the candidate would do on the job. There are no LeetCode-style general coding interviews. If you want to prepare for the interviews, we suggest looking at one of our papers or blog posts published in the last 12 months. Then either (a) replicate/extend the result hands-on, or (b) identify a conceptual limitation and write a shovel-ready project plan that would address it.

Your Privacy and Fairness in Our Recruitment Process: We are committed to protecting your data, ensuring fairness, and adhering to workplace fairness principles in our recruitment process. To enhance hiring efficiency, we use AI-powered tools to assist with tasks such as resume screening. These tools are designed and deployed in compliance with internationally recognized AI governance frameworks. Your personal data is handled securely and transparently. We adopt a human-centred approach: all resumes are screened by a human and final hiring decisions are made by our team. If you have questions about how your data is processed or wish to report concerns about fairness, please contact us at info@apolloresearch.ai.

## About Apollo Research

## Company Overview
- **One-liner**: Apollo Research is an AI safety organization dedicated to securing frontier AI systems by detecting and preventing scheming (deceptive alignment) through technical research, evaluations, monitoring products, and policy guidance.
- **Entity Type**: Private (Public Benefit Corporation)
- **Headquarters**: London, United Kingdom (with offices in San Francisco, California, and opening Washington, D.C.)
- **Founded**: 2023
- **Founders**: Not publicly available from sources

## Core Business
- **Primary industry**: AI safety research and assurance; security monitoring for AI agents
- **Target customers**: Frontier AI developers (e.g., OpenAI, major labs), governments and international bodies, enterprises deploying AI agents
- **Mission**: "Secure frontier AI systems across development, deployment, and governance" [apolloresearch.ai/about](https://www.apolloresearch.ai/about)

## Products & Services
- **Scheming Evaluations**: Behavioral and interpretability-based evaluations to detect whether frontier AI models exhibit deceptive alignment (scheming). Apollo runs these evaluations in partnership with major AI labs, including OpenAI, to assess models before public deployment. [apolloresearch.ai/about](https://www.apolloresearch.ai/about)
- **Watcher (Monitoring Product)**: A two-part product for controlling and securing coding agent deployments:
  - *Watcher Live* – Real-time monitor that identifies and blocks undesirable actions or steers agents back on track.
  - *Watcher Analyze* – Observability layer for reviewing all agent activity, analyzing failures, and receiving notifications. [apolloresearch.ai/blog](https://apolloresearch.ai/blog/apollo-update-may-2026)
- **Governance Advisory & Policy Playbooks**: Threat modeling, loss‑of‑control playbooks, and policy recommendations for governments (e.g., EU AI Office, US Congress, UN Advisory Body). Notable outputs include *The Loss of Control Playbook* and the *Behind Closed Doors* primer on internal deployment. [apolloresearch.ai/blog](https://apolloresearch.ai/blog/apollo-update-may-2026)

## Market Standing
- **Valuation**: Not publicly available
- **Key Metric**: Headcount of 28 employees (as of mid‑2026), growing 72% year‑over‑year [linkedin.com](https://linkedin.com/company/apollo-research-ai)
- **Notable Investors/Partners**: OpenAI (partnered for testing o1 and anti‑scheming interventions), EU AI Office, UN Advisory Body, US Congress [apolloresearch.ai/about](https://www.apolloresearch.ai/about)
- **Growth Signals**: Opened San Francisco office in 2026 with plans to grow to 10+ staff; opening Washington, D.C. office in June 2026; expanding from evals into a full “Science of Scheming” research agenda and a commercial monitoring product [apolloresearch.ai/blog](https://apolloresearch.ai/blog/apollo-update-may-2026)

## Competitive Advantages
- **Unique focus on “scheming”**: Apollo is one of the few organizations dedicated specifically to detecting and preventing deceptive alignment—a risk many labs now acknowledge.
- **First-mover evidence**: Published the first public evidence that frontier models can scheme in context, lending credibility and urgency to their work.
- **Deep lab partnerships**: Direct collaborations with frontier AI developers (e.g., OpenAI) allow Apollo to run pre‑deployment evaluations and influence safety practices.
- **Dual research‑product model**: Combining fundamental behavioral research with a commercial monitoring product (Watcher) creates a real‑world feedback loop and diversified funding.

## Strategic Focus
- **Science of Scheming**: Understanding how scaling trends (e.g., increased situational awareness, long‑horizon RL) drive scheming behavior in future models, rather than only evaluating current models.
- **Evaluation campaigns**: Running suites of behavioral and interpretability tests across frontier models to generalize findings from individual systems.
- **Monitoring productization**: Scaling Watcher as an enterprise tool for securing coding agents, with both immediate safety and long‑term oversight subversion use cases.
- **Policy influence**: Advising governments on loss‑of‑control, internal deployment, and automated AI R&D risks; establishing a D.C. presence to shape national security procurement and regulation.

## Why Work Here
- **Mission‑driven**: Employees work directly on preventing one of the most severe risks from advanced AI—scheming and loss‑of‑control [apolloresearch.ai/careers](https://www.apolloresearch.ai/careers).
- **Compensation**: Competitive salary, equity, pension/401(k) matching, visa sponsorship, and relocation support [apolloresearch.ai/careers](https://www.apolloresearch.ai/careers).
- **Culture**: In‑person collaborative culture with offices in London, San Francisco, and D.C. – “the best collaboration happens in person,” though flexible working hours and work‑from‑home days are offered [apolloresearch.ai/careers](https://www.apolloresearch.ai/careers).
- **AI‑native engineering**: Fluency with AI tools and coding agents is valued; the company expects an engineering mindset and real‑world production skills.
- **High impact**: Small, fast‑growing team (28 people) with outsized influence on frontier AI policy and lab safety practices.

## Sources
1. [apolloresearch.ai/about](https://www.apolloresearch.ai/about)
2. [apolloresearch.ai/careers](https://www.apolloresearch.ai/careers)
3. [linkedin.com](https://linkedin.com/company/apollo-research-ai)
4. [apolloresearch.ai/team](https://www.apolloresearch.ai/team)
5. [apolloresearch.ai/blog](https://apolloresearch.ai/blog/apollo-update-may-2026)

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