Apollo Research

Location: London & San Francisco Workplace: onsite 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 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” can be adapted by embedded evaluators. We want more breakthroughs like this. We are also developing a “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? 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 . 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, 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. ABOUT THE TEAM Alex Meinke leads the fundamental science team’s research agenda with guidance from Marius Hobbhahn, 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. 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.

Location: London & San Francisco Workplace: onsite 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 publicly 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 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.  Apollo has taken steps toward becoming an embedded evaluator at frontier labs. We want to hire experienced senior researchers with a strong track record of getting things done to execute our embeddedevaluationplans. KEY RESPONSIBILITIES If embedded evaluations happen, you will be on one of the following teams: On-site embedded evaluators: This team will spend weeks or months embedded at a frontier AI lab, where they will conduct interviews, review the lab’s evidence, gather new evidence, and write a report. They will execute a playbook quickly and adapt to unforeseen circumstances. We hold our embedded evaluators to the highest standards, so you will likely have to first prove yourself within Apollo before being sent into an embedded campaign. That is, you will probably start on the applied research team to gain context on embedded evaluations. For this team, we prefer candidates with the right to work in the US who can be based in San Francisco. Applied researchers: This team will be the force-multiplier behind embedded evaluations. They will prepare on-site evaluators before they are embedded and support them while they are embedded. They will do the conceptual work to determine what evidence would support a safety case, and write the playbook for on-site evaluators to optimize how fast they can gather evidence. They will develop tooling for embedded evaluators, both ahead-of-time and urgently on demand. They will be the bridge connecting fundamental research and the on-site evaluators. For example, they might convert the methodology of “Measuring Reward-Seeking via Contrastive Belief Updates” into turnkey code and procedures. This team can work from any of our offices. Both are high-stakes teams. On-site embedded evaluators should be exceptionally strong at quick execution under high pressure, and very strong at planning and research. Applied researchers should be exceptionally strong at planning and research, and very strong at quick execution under high pressure. For both teams, we are excited for experienced candidates who have owned ambitious projects end-to-end under tight deadlines. KEY REQUIREMENTS We don’t require a formal background or industry experience and welcome self-taught candidates. We assess demonstrated ability, including through independent research and nontraditional paths; prior formal management titles are not required. Technical safety research judgment: You have a strong track record of producing or leading technically rigorous research relevant to understanding and reducing risks from advanced AI. Your background might include alignment, interpretability, model evaluations, control, adversarial ML, or post-training. You understand modern model training well enough to interrogate a lab’s methods and learn unfamiliar pipelines quickly. Deep experience with frontier RL or post-training is valuable but not required. Research leadership and delivery: You have led ambitious technical projects end-to-end under hard deadlines and managed or closely supervised strong individual contributors (ICs). You can set research priorities, delegate meaningful ownership, review technical work, and keep a team focused on the questions that matter most. Data Analysis & Pattern Recognition: You can extract signal from large, messy datasets. You're comfortable with quantitative analysis and know when qualitative assessment is more appropriate. You can identify anomalies and unexpected model behaviors. Writing for high-stakes audiences: You have experience writing about qualitative and quantitative findings for different stakeholders. You can take a pile of unorganized technical information and communicate it effectively to lab safety teams, lab leadership, governments, or the public. Strong understanding of scheming: You have a strong conceptual understanding of ideas like metagaming, evaluation awareness, deception, and reward-seeking. You are familiar with classic literature like Is Power-Seeking AI an Existential Risk? and modern research. 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. Professionalism: You can represent Apollo independently in sustained, high-stakes technical collaborations.  Your professional conduct is unimpeachable. You know when to tactfully resolve disagreements with partners and when to stand firm in negotiations, and you are excellent at both of those. 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 . 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. Prior embedded expertise: You have previous experience in professional services like auditing or consulting. You’ve learned lessons from working on-site with clients that will generalize to working with frontier AI labs. 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. 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 prefer on-site embedded evaluators to be permanently located in San Francisco. Applied researchers can work from any of our offices. 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, 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. ABOUT THE TEAM Alex Meinke leads the science of scheming team’s research agenda with guidance from Marius Hobbhahn, though team members lead individual projects. You will mostly work with the science of scheming 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. 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 (a) thinking about what embedded evaluators should investigate, (b) building a tool that would be useful for embedded evaluators and running it on suitable proxy data, and/or (c) writing a report based on mock findings.  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.

Location: London Workplace: onsite THE OPPORTUNITY We are building Watcher, a coding agent security product. Watcher is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and multinational enterprises. We are hiring our founding Account Executive. You will own the sales cycle from qualified interest to signed contracts and build the playbook the rest of the GTM team runs on.  Our deals start with a security champion: a principal security engineer or director of product security who has seen what coding agents do inside their org and wants Watcher. Deals are won by taking that champion's interest and moving it through the rest of their organisation to a committed evaluation: the platform team that thinks it could build this itself, the CISO who has to sponsor it, the procurement and legal functions that have to let it in. That handoff, including the build-vs-buy conversation, is where deals stall today. Running it well is the core of this job. We are hiring forward-deployed engineers (FDEs) who run the technical evaluation with you. You own the commercial thread: who needs to say yes, what the evaluation has to prove, and how it converts. You'd work closely with our GTM lead and GTM associate who owns the top of the funnel. KEY RESPONSIBILITIES Convert a champion into an evaluation Find the champion in each qualified account, arm them with what they need to sell internally, and map who else has to say yes. Run the build-vs-buy conversation with platform and security engineering teams on its merits: what they would have to build, maintain and be on the hook for, against what Watcher already does. Secure an executive sponsor and convert interest into a scoped proof of value (POV) with success criteria agreed up front. Run evaluations to a decision, with FDEs Partner with FDEs on technical evaluations. They run the technical work; you keep the evaluation pointed at the decision: momentum, stakeholder coverage, and the commercial case. Build the commercial and ROI case that secures executive-level investment. Own commercial negotiation, security review, procurement and close in multi-stakeholder cycles involving security, engineering, procurement, legal and executive decision-makers. Create the way in Identify warm introductions paths via investors, advisors, clients, partners and networks. Brief connectors and draft the messaging that makes the introduction land. Capitalise on high-value engagement opportunities: speaking panels, hosted roundtables, buyer communities and embedded partners. Work with the GTM team on targeted outbound into segments where we have something specific to offer. Build the playbook and hire the team Write down what works: qualification criteria, discovery questions, the build-vs-buy argument, POV structure, pricing guidance, objection handling, security questionnaire answers. Report on pipeline health, stage conversion and what is working well enough to double down on. Product and market insights Bring honest signals back about why deals progress or stall, and what buyers compare us against when they consider building it themselves. Work with the product team on positioning, pricing, and the collateral enterprise buyers actually ask for. REPRESENTATIVE PROJECTS Turn a champion into a buyer. Take an account where a principal security engineer loves the product and nothing has moved for two months. Map who else needs to say yes, run the build-vs-buy conversation with the platform team, get an executive sponsor, and convert it into a scoped evaluation with success criteria and a date. Compress the cycle. Enterprise evaluations in this market routinely take longer than they need to. Take the process apart with the FDEs,  work out where the time actually goes, and rebuild it so an evaluation reaches a decision faster. Land a lighthouse enterprise . Take a large, slow, security-conscious organisation from first conversation through technical evaluation, security review and procurement to a production contract, and turn the process into a documented path the next one can follow. Write the playbook. By the time we add the next two people to this team, the thing that gets them productive in a month rather than six should be something you wrote. JOB REQUIREMENTS Must-haves 5+ years in GTM , with a track record of closing five- and six-figure ACV technical deals as an AE into engineering or security organisations. You have personally taken a technical champion's interest through the rest of their org to a signed contract, and you have won the build-vs-buy argument more than once. Proven success selling a technical product to technical buyers matters more to us than total AE tenure. Early-stage experience. You have sold at an early-stage company before it had a machine.You have 2+ years in a startup without mature marketing, no SDR team, no brand recognition doing the work for you. You generated your own pipeline, decided what to work on, and wrote down what worked. Technical credibility with a technical buyer . Our buyers are directors of product security, principal security engineers and engineering leaders. You can run discovery with them, understand the architecture well enough to be useful  and know when to bring in an FDE. Strong nice-to-haves You have closed into large, slow organisations. Our pipeline includes banks, quant funds and enterprise technology companies. You have run deals through security review, architecture review and procurement, and have a realistic sense of how long that takes and how to compress it. You have sold security, developer tools, or infrastructure. Non-linear background. Former founder, operator, or a hybrid role that combined selling with product or strategy. Explicitly not required Formal AI safety background. We need an excellent seller who can learn the AI safety context, and we will teach you what you need to know. Management experience. This starts as an individual contributor role. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Base Salary: £85,000 - £129,000; Variable: performance incentives against ARR targets Everyone at Apollo effectively has an unlimited token budget. If a better result costs more compute, use it. Flexible work hours and schedule 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 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) LOGISTICS Time Allocation: Full-time Location: This is an in-person role working out of our London office, with regular travel to conferences and customer sites. We offer flexible working hours and some wfh arrangements. Visa sponsorship: We sponsor visas in the UK. 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 THE TEAM You will work directly with Kyle Dai, our GTM lead and GTM associate on the pipeline, as well as the FDEs we're hiring to run the technical evaluations alongside you. Marius Hobbhahn (CEO) advises the team. You will also interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples, including anything you have written. About the interview process: Our multi-stage process includes a screening interview, a take-home exercise (2-3 hours), 2-3 technical interviews, and a final interview with Marius (CEO). If you want to prepare, we suggest using a coding agent heavily for a week and forming your own view on who should be buying something like Watcher, and why. 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. 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.

Location: London Workplace: onsite THE OPPORTUNITY We are building Watcher, a coding agent security product. Watcher is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and multinational enterprises. We are looking for our first Go-To-Market Associate to build Watcher's pipeline engine and bring a qualified pipeline to the Founding Account Executive.  Think of the role as a BDR / GTM engineer hybrid. Half the work is finding and reaching the right people inside the right companies. The other half is building the systems that make that repeatable: signal detection, enrichment, sequencing, reporting. A good chunk of this job should be automating the previous version of this job. There is no marketing function for Watcher today. Until there is, you own the parts of marketing that produce the pipeline: events, target personas, and getting our research in front of the people who should read it. Our ideal customer profile and target market are set together with product and the GTM lead. Within those accounts, you decide who we reach, how we reach them, and which experiments we run to find out which segments are real. You will work directly with our GTM lead. As we grow, this role can develop toward owning deals as an account executive, GTM engineering and operations, or partnerships, depending on where your strengths are. KEY RESPONSIBILITIES Build the pipeline engine Work out which companies are deploying coding agents at scale, who inside them feels the problem, and which of them we should spend time on. Research accounts and people. Work out whether we have something useful for this person and what it is. Personalize the outreach. Short, specific, honest messages that a busy security engineer will reply to. Run outbound as a set of experiments rather than a volume target: test segments, messages, and channels against each other, read the results honestly, and kill what is not working. Hand qualified conversations to the AE and GTM lead with the context they need to run them well. Pipeline operations and tooling Turn repeated manual work into tooling: signal detection for who has started deployment agents, enrichment, sequencing, CRM automation, and the reporting that shows what top of funnel is actually producing. Maintain best-in-class CRM hygiene: accurate notes, activity logging, dispositions, and next actions. Everyone has visibility where every live opportunity stands and what it is waiting on. Events and conferences Own the conference motion end to end. Decide which events are worth attending. Attendee research and prioritisation, pre-show outreach, booked meetings before the doors open, logistics on the day, and follow-up out within 48 hours. Make the team effective in the room: briefs on who is coming, what they care about, and what we want out of each conversation. REPRESENTATIVE PROJECTS Build the signal engine. Write the thing that tells us which companies started deploying coding agents at scale this month, from job posts, engineering blogs, change logs, conference talks and hiring patterns. Score them, attach the right persona, and drop them into the CRM ready to work. Own a conference end to end. Take a sponsored conference: work the attendee list, research and rank who is worth our time, run the pre-show outreach, land a target number of booked meetings before day one, and have every follow-up sent within 48 hours of the close. Write up the parts worth repeating. Make how we approach a new account repeatable. Turn the way we research and open a company into something anyone here could run, and then into something that mostly runs itself. JOB REQUIREMENTS Must-haves Around 2+ years in a role where you had to find and open conversations with people who did not know you, and nobody handed you a list: early-stage BDR or SDR, founder's associate, consulting where you were involved in business development or building a practice, solutions or customer-facing engineering, or running your own thing. You do not need to be a formally trained salesperson. If you've built pipeline from scratch in another context, we'd love to hear about it. You have a track record of building: You can show us something you built to avoid doing a task twice e.g. a scraper, an enrichment flow, a CRM automation, a vibe-coded internal tool. High agency and ownership. You see what needs doing and do it. When something stalls, you find out why and move it, rather than reporting that it is stalled. You want the responsibility of deciding where our effort goes, not just executing a list. Startup pace. Excited by a fast-moving environment, comfortable with ambiguity and changing priorities, and willing to grind when it matters. Technically curious. You need to get interested in how coding agents work and how they fail, well enough to hold a credible conversation with a security engineer and know when to hand off to someone deeper. Strong nice-to-haves Background in cyber security, developer tools, or AI/ML Comfortable leveraging AI heavily for tooling, research, drafting and operations, while holding the output to a high standard. Experience in founder-led sales. You have supported a founder or a first GTM hire directly and understand how that relationship works. Comfort with GTM tooling and light scripting. CRM, enrichment, outbound tooling, or anything you have built yourself to avoid doing something twice. Explicitly not required Formal AI safety background. We need an excellent operator who can learn the AI safety context, and we will teach you what you need to know. A sales title. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Base Salary: £72,000 - £98,000; Variable: performance incentives against ARR/qualified pipeline targets. Everyone at Apollo effectively has an unlimited token budget. If a better result costs more compute, use it. Flexible work hours and schedule 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 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) LOGISTICS Time Allocation: Full-time Location: This is an in-person role working out of our London office, with regular travel to conferences and occasional travel to customer sites. We offer flexible working hours and some WFH arrangements. Visa sponsorship: We sponsor visas in the UK. 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 THE TEAM You will work directly with Kyle Dai, our GTM lead, and alongside the our founding Account Executive who runs the deals your pipeline feeds. Marius Hobbhahn (CEO) advises the team. You will also interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples, including anything you have written. About the interview process: Our multi-stage process includes a screening interview, a take-home exercise (2-3 hours), 2-3 technical interviews, and a final interview with Marius (CEO). If you want to prepare, we suggest using a coding agent heavily for a week and forming your own view on who should be buying something like Watcher, and why. 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. 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.

Location: London Workplace: onsite ABOUT APOLLO RESEARCH Apollo Research is an AI safety organization that works on the detection of scheming (e.g., building evaluations), the science of scheming (e.g., model organisms), and scheming mitigations (e.g., anti-scheming and control). We work closely with multiple frontier AI companies. 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 read more here. We're a team of ~35 in London and Europe, we're opening a new office in San Francisco, and we're growing fast. THE OPPORTUNITY The Finance team consists of the Head of Finance and an interim transactional processor. You will be Apollo's second full-time Finance hire, reporting to the Head of Finance. You will own financial operations, accounting, reporting, and compliance. AI is embedded across the business, and Apollo Finance is no different. You will work with the Head of Finance to build a finance function from first principles that scales ahead of the business's growth. You'll be expected to build and leverage AI agents and clean architecture to eliminate, simplify, and automate manual work at scale. There is no legacy finance function to inherit, so you will build your subfunctions from the ground up. This role is suited to someone who is happy to get their hands dirty in the details of the operation, skilled at handling data, and driven to find more effective ways of doing things. The manual processing work should reduce significantly as you and the Head of Finance build out the systems, processes, and controls. Initially, your time will be split between: Day-to-day transactional processing and bookkeeping, replacing the interim processor (50%) Building out new systems, processes, and controls (25%) Eliminating, simplifying, and automating existing processes and controls (10%) Ad hoc work, e.g., monthly close, reporting, and compliance (15%) The team is deliberately lean and uses a sophisticated tech stack. This is not a position where you'll be one of many; you'll have significant exposure and be central to how the function evolves as the business scales, with a clear path to Financial Controller. RESPONSIBILITIES Run day-to-day financial operations (Order-to-Cash and Procure-to-Pay): Process and match supplier invoices, card expenses, and bank receipts and payments Prepare supplier payments Onboard customers and suppliers, keeping their metadata up to date Invoice customers and manage collections Run accounting and reporting (Record-to-Report): Establish a repeatable monthly close, preparing manual journals and accounting judgments on accruals, prepayments, accrued and deferred revenue, payroll, intercompany, and project accounting Prepare supported reconciliations for every balance sheet account, and maintain a quantified risks and opportunities list Produce a monthly pack that ties to the ledger and reads the way operators run the business: P&L, balance sheet, and cash flow, month and year to date; one headline variance against budget; and department and research-versus-commercial views Deliver cash visibility: Prepare the weekly 13-week direct cash forecast, including running scenarios for the Head of Finance and COO Reconcile forecasts to actual bank movements, explain and report variances, and improve the forecasting assumptions week on week Support the Head of Finance in building effective systems, processes, and controls: Eliminate, simplify, and automate manual work, processes, and controls at scale, leveraging AI agents and automation Document critical workflows so they do not depend on one person Manage the finance tech stack, and support the Head of Finance in implementing new systems Manage compliance: Support the Head of Finance in ensuring Apollo complies with relevant laws and regulations across the jurisdictions in which we operate, coordinating with advisors where necessary: statutory accounts, taxes, transfer pricing, R&D tax credits, audits where required, and expenditure responsibility requirements for grants WHAT SUCCESS LOOKS LIKE Build and run a best-in-class finance operation. Support the Head of Finance in building and improving the systems, processes, and controls for the core business cycles (O2C, P2P, R2R) to: (a) capture transactions at source with the right accounting, timing, and metadata; and (b) eliminate, simplify, and automate manual work at scale. Work month on month to reduce the lag between a transaction's economic event and its appearance in the books, and to reduce manual processing hours, baselined in month two. Maintain sales invoice accuracy at 100%, aged debtors below 15 days, and suppliers paid on the due date 100% of the time. Build and run a fast, dependable close. All entities closed to year-end quality every month: working day five within six months of joining, and working day three within nine months. Supported balance sheet reconciliations for all accounts, with a quantified risks-and-opportunities list. No restatements or surprises to the board. Build and prepare reporting that informs and forces decisions. A monthly pack within two months of joining that explains performance across P&L, balance sheet, and cash flow for the month and year to date, with department and research-versus-commercial views. It leads with the most important budget variance and explains what happened, why, and what needs attention. A weekly 13-week direct cash flow forecast, with scenarios, refreshed weekly. Forecast accuracy improves week on week, and within three months of joining the forecast informs and forces the right decisions on liquidity and working capital. Build and run the financial compliance calendar. Covers all relevant laws and regulations across every jurisdiction in which we operate, including R&D tax credits, audits, and expenditure responsibility requirements for grants. Compliance is fully up to date within three months of joining. REQUIREMENTS Sound accounting judgment. You are a qualified accountant (ACA, ACCA, CIMA, or CPA) with at least two years' post-qualification experience at a small, UK company. You reason confidently through cut-off, accrued and deferred revenue, accruals and prepayments, payroll, and project accounting. You know when to ask for specialist advice rather than guess. You are skeptical by default. Personal ownership of a fast close. You have personally (not through a team) run monthly closes end to end to working day five, with supported reconciliations for every balance sheet account. You have personally produced multi-dimensional management accounts (product, channel, region) with variances to budget, built around how operators run the business. Balance-sheet-first mindset. For any account, you can explain what the number is, how it got there, how and when it settles or unwinds, and what happens if it does not. International group experience. You have practical experience of intercompany accounting and consolidation, you have worked with at least one overseas entity (US preferred), and you know UK statutory reporting. Hands-on builder. You have built, materially improved, or repaired a finance process, control, or set of books. You prioritize the material problems. You are a high-agency, first-principles problem solver who can take a problem, break it down logically, and find a solution. You are comfortable making decisions with incomplete information. Fast, accurate transactional processing. You process invoices, card receipts and bills, and cash receipts and payments. Architecture-minded and data-literate. You think in systems, not processes. You are fluent in a cloud ledger (Xero, QuickBooks Online, Campfire, Light, Rillet, or DualEntry) and strong in spreadsheets. You understand how data and workflows fit together. You can validate integrations, imports, mappings, and automated outputs. High standards. You have exceptional attention to detail, integrity, and strong numerical ability. You are tenacious enough to call a customer for the fifteenth time. You communicate clearly with non-finance colleagues. NICE-TO-HAVES 2+ years at a VC-backed or founder-led high-growth scale-up (Series A to C), ideally through a first audit. You know how a young company works. 2+ years in deep-tech, R&D-heavy, or software businesses. You are comfortable with R&D tax credits, grant reporting, capitalization judgments, cloud and compute costs. 2+ years' experience with project or professional services accounting. You are comfortable with subscription, license, or usage-based billing and revenue recognition. Practical experience simplifying and automating processes, controls, and workflows, and implementing AI tooling in a live finance function Experience coordinating UK and US accounting and tax providers. 1+ years' experience with a modern AI-native ERP (Campfire, DualEntry, Rillet, or Light). Demonstrable experience using AI agents and automation daily, as well as deploying them in real finance functions. You are comfortable using no-code tools (n8n, Zapier), MCP, and coding agents. Interest in AI safety and Apollo's mission. You're curious about what we do and why it matters. BENEFITS This role offers market competitive salary and competitive benefits. Salary: £107,000–£145,000 Flexible work hours and schedule Unlimited vacation Unlimited sick leave Up to six months of paid parental leave Comprehensive health, dental, and vision insurance Retirement savings with competitive employer matching Lunch, dinner, and snacks are provided on workdays Paid work trips, including staff retreats, business trips, and relevant conferences A yearly $1,000 (USD) professional development budget LOGISTICS Target start date: Late Q4 2026 to early Q1 2027. We can be flexible to accommodate notice periods. Role type: Full-time Location: In-person at our London office, with flexible hours and some WFH arrangements Reports to: Head of Finance Right to work: Applicants must have the right to work in the UK. We are unable to provide visa sponsorship for this role. HIRING PROCESS Apply using the application form and attach your CV. We review applications on a rolling basis, so we encourage you to apply early. Initial conversation (30 minutes) Take-home assignment (3 hours) Technical interview #1 with the Head of Finance (1 hour) Technical interview #2 with the Head of Finance (1 hour) Interview with the COO (1 hour) Offer and reference checks 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. 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.

Location: London & San Francisco Workplace: onsite THE OPPORTUNITY THE OPPORTUNITY Apollo Research works with most frontier AI companies (OpenAI, Anthropic, Google, Meta, Thinking Machines and others) to test their models before deployment and collaborate on fundamental scheming research. Our coding agent security product, Watcher, is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and enterprises.  We are looking for a security & control expert  to help us design better threat models and control protocols against AI adversaries, and improve the effectiveness and security of Watcher.  This is truly a "start-up role" in the sense that you have significant say in shaping the direction of the role. This is an individual contributor role but could lead to management responsibilities eventually, if desired. KEY RESPONSIBILITIES Threat modeling for coding agents Develop threat models for coding agents under the assumption that they could be misaligned or compromised. This includes near-term threats like AI agents leaking private internal data and long-term threats like agents sabotaging safety research. Treat coding agents through the lens of insider risk: they have credentials, access to code, network access, and the ability to execute arbitrary actions, just like a malicious insider would (see Control agenda ). Map out kill chains and attack progressions similar to frameworks like MITRE ATT&CK, adapted for agentic AI. See e.g. the Agentic Loss-of-Control Threat Matrix for an example of a high-quality contribution. Build and maintain our “coding agent security levels” which define what level of robustness Watcher provides against different categories of failure modes. These levels should be concrete, testable, and usable both internally (to guide product priorities) and externally (to communicate our security posture to customers). Failure mode library and literature Maintain our library of coding agent failure modes and keep it comprehensive, accurate, and current. Stay up to date with the academic and industry literature on coding agent security, AI security, and adjacent fields (prompt injection, deception, accidental security implications, supply chain attacks on AI systems, etc.). Attack design for monitor development Design realistic attack trajectories and example exploits for monitor development and backtesting, work with the research team to ensure monitors are trained and evaluated against credible adversarial behavior. Trajectory adjudication Serve as security adjudicator for flagged trajectories. When monitors flag ambiguous agent behavior, determine whether it represents a genuine attack, a benign-but-unusual workflow, or needs deeper investigation, and feed those judgments back into monitor improvement. Red-teaming Watcher Red-team and improve Watcher's monitors and policies, document findings, and feed them into product improvement. Work with the technical research staff to build adversarial test suites into the backtesting pipeline. *This role is distinct from the AI Security Researcher role on Apollo's Infra & Security team, which threat-models Apollo's own internal systems and the agents operating on them. REPRESENTATIVE PROJECTS Develop a comprehensive coding agent threat model: Think deeply about all the ways in which coding agents could attack an organization. Compare this threat model to conventional threats from human insiders. Publish a detailed research piece describing the threat model building on existing research, e.g. from Redwood Research. Improve our database of failure modes: We have an internal database of 50+ failure modes of coding agents with detailed reports for all of them. For this project, you would provide an expert view on the current state of that database and suggest improvements. In the long run, you would maintain that database and be responsible for integration of new failures. Prioritize failure modes that Watcher should cover: Different parts of Watcher attempt to cover different threat models and attack strategies. Based on the results of the threat model project above, we want to ensure that each part of Watcher covers the most important failure modes in the most efficient way. For example, not all monitors require blocking capability and some failure modes might benefit from additional affordances like being able to disperse subagents. JOB REQUIREMENTS Must-haves 5+ years in security roles in a hands-on technical capacity (not purely GRC/compliance). You need to be able to think structurally about threat modeling and failure modes. You need to be able to read code, understand infrastructure, and evaluate technical controls, not just write policies. Direct experience with security research, threat modeling, or offensive security. Comfortable working in a small, fast-moving team, and comfortable owning ambiguous, open-ended research questions. Strong written communication this role produces threat models, security levels, and failure mode documentation that need to be clear and precise Nice-to-haves Experience with AI/ML systems security, LLM security, or AI control research. The field is young enough that deep experience here is rare, but any exposure significantly reduces ramp-up time. Detection engineering, SOC, or incident analysis experience. A part of this role is judging whether flagged agent behavior is genuinely malicious, and people who have triaged real-world alerts might ramp much faster. Familiarity with insider threat programs or insider risk frameworks. The mental model of "the coding agent is a potentially malicious insider" is useful for this role and someone who has worked on insider threats will pick it up faster. Red teaming or offensive security background. Useful for the Watcher red-teaming responsibilities and for thinking adversarially about failure modes. Formal AI safety research background. Helpful but not necessary. We need security practitioners who can learn the AI safety context, not AI safety researchers who need to learn security. Explicitly not required Management experience. This is an IC role, at least initially. Specific certifications (CISSP, etc.). We care about demonstrated ability, not credentials. 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. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Salary: San Francisco : $204,000 – $385,000; London : £136,000 – £258,000. Flexible work hours and schedule 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) 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 some wfh arrangements. 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 THE TEAM The product team consists of research scientists: Victor Gillioz, Monika Jotautaitė, Dmitrii Volkov; product engineers: Jeremy Neiman, Zak Walters, Zen van Riel, Srdjan Miletic and Gustavo Bicalho; and our GTM lead: Kyle Dai. Marius Hobbhahn (CEO) advises the team. Furthermore you will interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, a take-home test (3 hours), 3 technical interviews, and a final interview with Marius (CEO). There are no leetcode-style general coding interviews. You may use AI tools on the take-home; we judge the result the way we'd judge any contributor's work, so you are responsible for the quality of everything you submit. If you want to prepare, we suggest building simple monitors for coding agents and running them on your own Claude Code / Cursor / Codex / etc. traffic. 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. 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.

Location: London & San Francisco Workplace: onsite THE OPPORTUNITY    Join our new AGI safety product team and help transform AI control research into practical tools that directly reduce risks from AI. As an Research Scientist (Control), you’ll work closely with Marius (CEO & currently leads the monitoring efforts), other control researchers and product engineers.    We are currently building Watcher,  a monitoring tool for coding agents. Our monitoring research agenda attempts to translate compute into safety at scale. You will join a small team and will have significant ability to shape the team & tech, and have the ability to earn responsibility quickly.  You will like this opportunity if you're passionate about using empirical research to make AI systems safer in practice. You enjoy the challenge of translating theoretical AI risks into concrete detection mechanisms. You thrive on rapid iteration and learning from data. You want your research to directly impact real-world AI safety. KEY RESPONSIBILITIES TLDR: you will design & implement control protocols (see e.g. [Greenblatt et al, 2023] ) and test them on real-world production systems at scale. Research & Development Design and conduct experiments to test monitor effectiveness across different failure modes and agent behaviors Build and maintain evaluation frameworks to measure progress on monitoring capabilities Build and maintain high-quality datasets to train and test monitors on Iterate on monitoring approaches based on empirical results, balancing detection accuracy with computational efficiency Contribute newly discovered coding agent failure modes to Apollo's shared failure mode library (owned by the AI Security & Control Researcher/Engineer) as they surface during monitor design, evaluation, and iteration Stay current with research on AI safety, agent failures, and detection methodologies Stay current with research into coding security and safety vulnerabilities Fine-tuning & Monitor Development Fine-tune open-source models to create efficient monitors for high-volume production environments Design and build agentic monitoring systems that autonomously investigate logs to identify both known and novel failure modes Incorporate adversarial findings from red-teaming campaigns into monitor design and evaluation, closing the loop between attack and defense JOB REQUIREMENTS Must-haves 2+ years of experience conducting empirical research with large language models or AI systems Strong experience with AI coding agents. For example, having extensively used and compared frontier coding agents, or having designed / developed coding agents Experience with LLM-as-a-judge setups or AI monitoring more broadly Experience designing and running experiments, analyzing results, and iterating based on empirical findings e.g. prompting, scaffolding, agent design, fine-tuning, or RL Strong Python programming skills Demonstrated ability to work independently on open-ended research problems Good-to-haves Familiarity with AI safety concepts, particularly agent-related risks Familiarity with computer security, e.g. security testing and secure system design Experience fine-tuning language models or working with smaller open-source models Previous work building developer tools or monitoring systems Publications or contributions to AI safety or ML research Experience with production log systems or production log analysis 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. REPRESENTATIVE PROJECTS Hierarchical monitoring for coding agent security : Design a multi-layer monitoring system for detecting security vulnerabilities introduced by coding agents. Start by cataloging common security failure modes (e.g., hardcoded credentials, SQL injection vulnerabilities, insecure API calls). Build specialized monitors for each category, then create a hierarchical system where fast, efficient first-pass monitors flag potentially problematic code for deeper investigation by more sophisticated monitors. Design a backtesting strategy for Watcher: Validate the system on synthetic test cases and real agent outputs, iterating to optimize the tradeoff between detection rates and false positives while maintaining low latency for most monitoring decisions. Fine-tune an open-source model to be a great monitor: Take an open-source model and fine-tune it on our large dataset of coding agent failures with high-quality ground truth labels. Test different fine-tuning techniques and measure generalization to our held-out data. Compare against prompted baselines on accuracy, cost and latency. The goal is to fine-tune models to lift the pareto frontier of monitors. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Salary: San Francisco : $204,000 – $385,000; London : £136,000 – £258,000. We will be looking to meaningfully raise salaries soon. Flexible work hours and schedule 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) 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. 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 THE TEAM The product team consists of research scientists: Victor Gillioz, Monika Jotautaitė, Dmitrii Volkov; product engineers: Jeremy Neiman, Zak Walters, Zen van Riel, Srdjan Miletic and Gustavo Bicalho; and our GTM lead: Kyle Dai. Marius Hobbhahn (CEO) advises the team. Furthermore you will interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, a take-home test (3 hours), 3 technical interviews, and a final interview with Marius (CEO). There are no leetcode-style general coding interviews. You may use AI tools on the take-home; we judge the result the way we'd judge any contributor's work, so you are responsible for the quality of everything you submit. If you want to prepare, we suggest building simple monitors for coding agents and running them on your own Claude Code / Cursor / Codex / etc. traffic. 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. 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.

Location: London & San Francisco Workplace: onsite THE OPPORTUNITY Apollo Research works with most frontier AI companies (OpenAI, Anthropic, Google, Meta, Thinking Machines and others) to test their models before deployment and collaborate on fundamental scheming research. Our coding agent security product, Watcher, is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and enterprises.  Security exists at Apollo to safeguard the trust our lab partners place in us and to enable that research. Our own team runs around 400 billion tokens a month through AI agents, which makes Apollo both a target and a testbed. We’re hiring Security Engineers to join the Infra & Security Team.  In this role, you will design, implement, and deploy the security controls that keep Apollo secure, and make the secure path the default path for everyone. You will build the security layer for a novel insider risk that didn't exist before. RESPONSIBILITIES Design, build, and operate Apollo's core security controls: identity and access management, infrastructure and cloud security, endpoint management, and incident response. Build paved roads that make the secure path the default, easy path Establish and advocate for security practices across Apollo, and build the organizational trust needed for people to adopt them, communicating security decisions and tradeoffs clearly along the way. Evaluate vendors and tooling, make build-vs-buy calls on security infrastructure, and assess vendor security posture before adoption. Lead major security projects end to end, from ideation through implementation and rollout. Collaboratively define and shape the security roadmap and priorities. KEY REQUIREMENTS Must-haves 5+ years in security roles in a hands-on technical capacity (not purely GRC/compliance). You'd need to be able to think structurally about threat modeling and failure modes. You need to be able to read code, understand infrastructure, and evaluate technical controls. Engineering mindset. You treat security as an engineering problem. You are capable of, and willing to, build custom solutions when the task demands it, rather than relying on 'glueing' together off-the-shelf tools. You prioritize automation and systems-level thinking to scale security, and you are comfortable leveraging AI to accelerate development. Direct experience with application security, cloud security, or product security. Ideally you’ve owned or significantly contributed to the security posture of an organisation that handles sensitive customer data. Influence without authority. You have a track record of building buy-in for security practices and being trusted by non-security people. Startup pace. You are excited about a fast-moving environment, comfortable with ambiguity and changing priorities, and willing to grind when it matters. Nice-to-haves Software engineering experience We would be excited to have candidates who have the one or more of these specialisations: Zero-trust architectures Sandboxing Detection engineering GRC engineering Incident response Endpoint management (Kandji, Jamf, or similar) Identity/access management Explicitly not required Deep AI/ML security or LLM-specific expertise Formal AI safety research background. We need security practitioners who can learn the AI safety context, not AI safety researchers who need to learn security. 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. REPRESENTATIVE PROJECTS Zero trust migration: Design and deliver an incremental path from Apollo's current cloud architecture toward zero trust, including hands-on implementation and deployment reducing the blast radius if any single credential, device, or agent is compromised. Agentic SOC: Design and build an internal agentic SOC from scratch; automate triage and investigation of security alerts so detection coverage scales with Apollo's growing use of AI agents. Sandbox hardening: Review Apollo's sandboxing infrastructure; propose and implement changes that improve security while minimizing impact on researcher productivity. Third-party access review: Implement a lean, streamlined access review process for third-party software that people will actually use. Incident response: Lead an incident response effort end to end: containment, root cause, and the post-mortem that turns it into a durable fix. Pentesting tooling: Decide which agentic pentesting solution Apollo will use, and whether to build or buy. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Salary: San Francisco : $214,000 – $280,000; London : £144,000 – £189,000. We will be looking to meaningfully raise salaries soon. Our engineers effectively have an unlimited token budget. If a better result costs more compute, use it. Flexible work hours and schedule 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) 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 some wfh arrangements. 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 THE TEAM The Infra and Security team is currently led by Rusheb Shah and consists of Glen Rodgers and Steven Lee. You will work closely with Security Researchers we’re hiring for as well as technical staff from the Scheming Research and Product team. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, a take-home test (approx. 2-3 hours), 3 technical interviews, and a final interview with Marius (CEO). There are no leetcode-style general coding interviews. You may use AI tools on the take-home; we judge the result the way we'd judge any contributor's work, so you are responsible for the quality of everything you submit. If you want to prepare, we suggest building simple monitors for coding agents and running them on your own Claude Code / Cursor / Codex / etc. traffic. 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. 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.

Location: London & San Francisco Workplace: onsite THE OPPORTUNITY Apollo Research works with most frontier AI companies (OpenAI, Anthropic, Google, Meta, Thinking Machines and others) to test their models before deployment and collaborate on fundamental scheming research. Our coding agent security product, Watcher, is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and enterprise.  Security exists at Apollo to safeguard the trust frontier labs place in us and to enable that research. Our own team uses AI agents extensively across its work, which makes Apollo both a target and a testbed. We’re hiring AI Security Researchers to join the Infra & Security Team.  In this role, you will identify, research and remediate both conventional threats and the novel risks introduced by AI agents that can affect Apollo and our mission. You will redefine and work on a new class of insider risk that didn’t exist before. RESPONSIBILITIES Hold responsibility for the security of Apollo’s internal surfaces. Red-team internal software, infrastructure, and AI agent access controls/monitoring. Build realistic attack trajectories. Design solutions for novel or emerging threats in the AI security space, where no existing playbook applies. Set the standard for our security posture in these areas. Track adversary tactics, techniques, and campaigns relevant to Apollo's threat landscape, and translate that intelligence into tuned, high-signal detections. Own each finding through to a deployed fix. Build and roll out durable controls: checks, tests, defaults, detections. Socialise them, work with engineers to implement, and hold remediation to a high bar. KEY REQUIREMENTS Must haves 5+ years in security roles in a hands-on technical capacity (not purely GRC/compliance). You'd need to be able to think structurally about threat modelling and failure modes. You need to be able to read code, understand infrastructure, and evaluate technical controls. Direct experience with offensive security. Threat modelling, red teaming, etc. Knowledge of application or cloud. Ideally you owned or significantly contributed to the security posture of an organisation or product that handles sensitive customer data. Engineering mindset. You treat security as an engineering problem. You can translate your findings into fixes and controls, such as paved roads, custom detection rules, adversarial test suites, CI/CD integrations. You prioritize automation and systems-level thinking to scale security, and you are comfortable leveraging AI to accelerate development. Startup pace. You are excited about a fast-moving environment, comfortable with ambiguity and changing priorities, and willing to grind when it matters. Strong written communication. This role produces a lot of artifacts (threat models, reports, failure mode documentation) and they need to be clear and precise. Nice to haves Experience with AI/ML systems security or LLM security. Detection engineering, SOC, or incident analysis experience. Familiarity with insider threat programs or insider risk frameworks. Explicitly not required Formal AI safety research background. We need security practitioners who can learn the AI safety context, not AI safety researchers who need to learn security. 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. REPRESENTATIVE PROJECTS Red-team Apollo's agent sandboxes used for evals: Test whether an agent can escape isolation, exfiltrate data, detect it's being evaluated, or otherwise undermine the validity of eval results. Your findings will harden the sandbox infrastructure the research team depends on to trust its own eval results. Comprehensive coding agent threat model : Map every way a coding agent with internal access: credentials, code, network, execution ability,  could attack Apollo, benchmarked against what a human insider with the same access could do. As well as the representative projects from the Security Engineer role BENEFITS This role offers market competitive salary, equity, and competitive benefits. Salary: San Francisco : $214,000 – $280,000; London : £144,000 – £189,000. We will be looking to meaningfully raise salaries soon. Our engineers effectively have an unlimited token budget. If a better result costs more compute, use it. Flexible work hours and schedule 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) 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 some wfh arrangements. 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 THE TEAM The Infra and Security team is currently led by Rusheb Shah and consists of Glen Rodgers and Steven Lee. You will work closely with Security Engineers we’re hiring for as well as technical staff from the Scheming Research and Product team. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, a take-home test (approx. 2-3 hours), 3 technical interviews, and a final interview with Marius (CEO). There are no leetcode-style general coding interviews. You may use AI tools on the take-home; we judge the result the way we'd judge any contributor's work, so you are responsible for the quality of everything you submit. 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. 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.

Location: London & San Francisco Workplace: onsite THE OPPORTUNITY We are building Watcher, a coding agent security product. Watcher is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and multinational enterprises. Every one of those deployments involves custom engineering: integrating with the customer's coding agents and security stack, running in their cloud or on-prem environment, and tuning monitors to their workflows. We are looking for a Forward Deployed Engineer to own that work end to end. You will be the technical face of Watcher to customers across the entire lifecycle: running technical evaluations and POCs with prospects, deploying Watcher into customer environments, building the integrations and custom capabilities they need, and providing white-glove support that turns early customers into long-term partners. You are an engineer first. When a customer needs something, your default is to build it, and then to work with the product team to decide what becomes part of the core product. This is truly a "start-up role." You will be one of the first dedicated customer-facing engineers, you will have significant say in shaping how Apollo deploys and supports Watcher, and you can earn more responsibility quickly. As we grow, this role can develop toward leading forward-deployed engineering, solutions architecture, or core product engineering, depending on your interests. KEY RESPONSIBILITIES Deployment and integration engineering (~40%) Own customer deployments end to end: cloud, on-prem, and self-hosted backends, from architecture discussion through production rollout. Build integrations between Watcher and customer environments: coding agent setups (Claude Code, Codex, Cursor), SIEM and security operations tooling, identity and access systems, CI/CD pipelines. Configure and tune monitors, policies, and permissions for each customer's workflows, and validate that monitoring quality holds on their real traffic. Debug issues in environments you don't control, often under time pressure, and drive them to resolution with the product team. Pre-sales and technical evaluation (~30%) Run technical evaluations and POCs with prospects: scope the pilot, deploy Watcher on their agent traffic, and demonstrate concrete value (caught failures, security coverage, developer experience). Serve as the technical counterpart to security and engineering leaders during evaluations: answer architecture and security questions, run demos on the prospect's actual use cases rather than canned examples. Work with our AI Security & Control Engineer on security questionnaires, architecture reviews, and customer pen-test support during procurement. Feed honest signal back into the team about why deals progress or stall technically. White-glove support and product feedback (~30%) Be the named technical contact for key customers. Own their escalations, incident communication, and feature requests. Build customer-specific capabilities where warranted, and drive the "should this be product?" conversation with the Engineering Manager and product engineers so one-off work compounds into the roadmap. Turn deployment experience into leverage: documentation, deployment tooling, and runbooks that make every subsequent deployment faster and less bespoke. Represent the voice of the customer in planning: you will know better than anyone what real security and platform teams need from Watcher. REPRESENTATIVE PROJECTS Land a lighthouse enterprise: Run a POC for a large enterprise with a strict security posture: scope the pilot with their security team, stand up a self-hosted backend inside their infrastructure, integrate with their SIEM, tune monitors on their traffic, and present results that convert the pilot into a production contract. Build a reusable deployment path: After your first several deployments, turn the bespoke steps into a repeatable, documented, partially automated deployment package (infrastructure templates, integration modules, validation checks) that cuts time-to-production for new customers by more than half. Ship a customer-driven capability into the product: Take a recurring customer request (e.g. a custom alert routing scheme or a new agent integration), build it for the customer who needs it, generalize it with the product team, and ship it as a core Watcher feature. Rescue a wobbly account: A customer's monitors are noisy, their engineers are annoyed, and their security lead is questioning the renewal. Diagnose the root causes of their traffic, retune the configuration, fix the underlying issues with the product team, and win back their confidence. JOB REQUIREMENTS Must-haves Strong engineering foundation. 3+ years building and shipping production software. You can work across the stack (Python and TypeScript primarily), read unfamiliar code fast, and are comfortable with cloud infrastructure, networking basics, and deployment tooling. You will regularly build under constraints you don't choose, in environments you don't control. Genuinely good with customers. You communicate clearly with both hands-on engineers and security leadership, you stay calm and constructive when something is on fire, and customers come away trusting you. This is not a tolerance requirement; you should like this part of the job. High agency and ownership. When a customer is blocked, you don't wait for a ticket to be routed. You find the problem, fix it or escalate it with a proposed solution, and follow through until it's resolved. Startup pace. You are excited about a fast-moving environment, comfortable with ambiguity and changing priorities, and willing to grind when it matters, including occasionally at customer-driven hours. Judgment on custom vs. core. You can tell the difference between a request worth building bespoke, a request that should become a product, and a request to push back on, and you can deliver that pushback to a customer gracefully. Strong written communication. Deployment docs, incident updates, and POC reports need to be clear and precise. Comfortable leveraging AI heavily for engineering and customer work, while holding the output to a high standard. Strong nice-to-haves Prior FDE, solutions engineering, or professional services experience, especially at a developer-tools, security, or infrastructure company. Experience deploying software into enterprise environments: on-prem, VPC deployments, SSO/identity integration, security reviews, procurement processes. Familiarity with the enterprise security stack (SIEM, DLP, endpoint tooling) and with how security teams evaluate vendors. Experience with coding agents as a power user or builder. You have used Claude Code, Codex, Cursor, or similar tools heavily, or built agent tooling yourself. Pre-sales exposure: you have run POCs or technical evaluations and know how to demonstrate value on a prospect's terms. Willingness to travel occasionally to customer sites. Explicitly not required Formal AI safety background. We need excellent customer-facing engineers who can learn the AI safety context. Sales experience or quota history. You support deals technically; you don't carry a number. Deep expertise in every technology we use. We care about demonstrated ability to learn fast in unfamiliar environments, which is the core skill of this job. 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. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Salary: San Francisco : $222,000 - $290,000; London : £149,000 - £195,000. We will be looking to meaningfully raise salaries soon. Our engineers effectively have an unlimited token budget. If a better result costs more compute, use it. Flexible work hours and schedule 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) LOGISTICS Time Allocation: Full-time Location: This is an in-person role working out of our London or San Francisco office, with occasional travel to customer sites. We offer flexible working hours and some wfh arrangements. 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 THE TEAM The product team consists of product engineers: Jeremy Neiman, Zak Walters, Zen van Riel, Srdjan Miletic and Gustavo Bicalho; research scientists: Victor Gillioz, Monika Jotautaitė, Dmitrii Volkov; and our GTM lead: Kyle Dai. Marius Hobbhahn (CEO) advises the team. Furthermore you will interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, a take-home test (3 hours), 3 technical interviews, and a final interview with Marius (CEO). There are no leetcode-style general coding interviews. You may use AI tools on the take-home; we judge the result the way we'd judge any contributor's work, so you are responsible for the quality of everything you submit. If you want to prepare, we suggest building simple monitors for coding agents and running them on your own Claude Code / Cursor / Codex / etc. traffic. 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. 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.

Location: London & San Francisco Workplace: onsite THE OPPORTUNITY We are building Watcher, a coding agent security product. Watcher is deployed in production and monitors billions of agent tokens per month across engineering teams at agent-building scale-ups and enterprises. We are looking for a product engineer to build and scale Watcher end to end. This role includes backend and frontend components. You will own features across the full stack, from agent hooks and ingestion pipelines to monitoring dashboards and enterprise integrations. You can expect to  own features from customer conversation to production deployment. This is truly a “start-up role”: you will own big chunks of the product, make decisions fast, and ship at high volume. You will join a small team with significant ability to shape the product and tech, and you can earn more responsibility quickly. This is an individual contributor role but could lead to management responsibilities eventually, if desired. KEY RESPONSIBILITIES Ship product end to end (~45%) Feature development Design, build, and ship Watcher features across the stack: agent-side hooks and CLI tooling, ingestion and grading pipelines, real-time monitoring UI (Watcher Live), and the organization-wide Analyzer dashboard. Own features end to end, from ambiguous requirements to production: talk to users, scope the work, make the technical decisions, put that into production, and iterate based on usage. Build Watcher integrations:  We currently support Claude Code and Codex, but we’re planning on increasing our coverage to other coding agents, LLM gateways, and anywhere that could benefit from agent monitoring. Build the policy and configuration layer that lets security teams set org-wide guardrails for  engineers to operate safely within. Productionize research Turn monitoring research (new monitors, grading strategies, backtesting results) into production-ready product features. We are "our own customer": You will use the product you build every day. Infrastructure and scale (~35%) Design and operate backend systems that process large volumes of agent logs in real time. Own reliability: robust error handling, graceful degradation, and observability (logging, metrics, tracing, alerting). We should catch issues before customers do. Architect data models and storage that handle both high-throughput writes and complex analytical queries over historical trajectories. Retention policies have to fit highly sensitive customer data. Make sure Watcher is safe to deploy: Work with our AI Security & Control Engineer on tenant isolation, encryption, and access controls. Watcher touches some of the most sensitive data our customers have. Customer and integration engineering (~20%) Build and maintain integrations with the enterprise security stack, e.g. streaming monitoring alerts into SIEM systems and existing security operations workflows. Support flexible deployment models: cloud-hosted, on-prem, and local backends, so sensitive agent logs never have to leave customer control. Talk to customers, debug issues in their environments, and feed what you learn back into the roadmap. REPRESENTATIVE PROJECTS Secure a new frontier coding agent: Take an agent we don't yet support (e.g. Cursor) from zero to fully monitored. Build the hook/integration layer, normalize its trajectory format into our data model, validate monitor accuracy on real traffic, and ship it to customers. Build the real-time blocking path: Design the low-latency pipeline that lets monitors block dangerous actions (e.g. git push --force, secret exfiltration) before they execute, while keeping p95 latency low enough that developers don't feel it. Handle traffic spikes and partial failures gracefully. Ship an Analyzer capability end to end: Design and build an organization-wide view (e.g. failure trends over time, cross-session pattern detection) from data model through API to UI, based on what security teams actually need. Deploy Watcher inside locked-down enterprises: Build and document the self-hosted/on-prem deployment story so that enterprises with strict data requirements can run Watcher  inside their own infrastructure. JOB REQUIREMENTS Must-haves 4+ years building production software. You have shipped and operated real systems with real users, and you know what it takes to keep them reliable. Full-stack capability. You are strong on the backend (APIs, data pipelines, databases, cloud infrastructure) and productive on the frontend (modern web UI). You don't need to be world-class at both, but you must be able to own a feature across the whole stack. Our stack is primarily Python and TypeScript. High agency and ownership. You are willing to own big chunks of the product, make decisions fast with incomplete information, and be accountable for the outcome. You don't wait to be told what to do and have an accurate sense of the roadmap for the product. High output volume. You have used Claude Code, Codex, Cursor, or similar tools heavily to accelerate or have built agents or agent tooling yourself. Startup pace. You are excited about a fast-moving environment, comfortable with ambiguity and changing priorities, and willing to grind when it matters. Product sense. You can talk to users, translate vague requirements into concrete designs, and make good calls about what to build and what to cut. Strong nice-to-haves Previous work on developer tools, monitoring/observability systems, or security products. Watcher sits at the intersection of all three. Real-time or large-scale data processing experience (streaming pipelines, message queues, high-throughput log systems). Enterprise or on-prem deployment experience. Shipping software into environments you don't control is its own skill. Experience building LLM-powered applications, e.g. LLM-as-judge setups, evaluation pipelines, or familiarity with frameworks like Inspect. Early-stage startup experience. You have been employee 1-15 somewhere and know what it feels like to build a product with no playbook. Explicitly not required Formal AI safety background. We need excellent product engineers who can learn the AI safety context, not AI safety researchers who need to learn engineering. Management experience. This is an IC role, at least initially. Deep experience in every technology we use. We care about demonstrated ability to learn and ship, not checkbox familiarity with our exact stack. 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. BENEFITS This role offers market competitive salary, equity, and competitive benefits. Salary: San Francisco: $222,000 – $290,000 London: £149,000 – £195,000. We will be looking to meaningfully raise salaries soon. Our engineers effectively have an unlimited token budget. If a better result costs more compute, use it. Flexible work hours and schedule 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) 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 some wfh arrangements. 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 THE TEAM The product team consists of product engineers: Jeremy Neiman, Zak Walters, Zen van Riel, Srdjan Miletic and Gustavo Bicalho; research scientists: Victor Gillioz, Monika Jotautaitė, Dmitrii Volkov; and our GTM lead: Kyle Dai. Marius Hobbhahn (CEO) advises the team. Furthermore you will interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work. You can find our full team here. ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, 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 science of scheming, detection of scheming (e.g. building evaluations), and scheming mitigations (e.g. anti-scheming). We also work on control and monitoring research (see our scalable monitoring agenda). We work closely with many frontier AI companies, such as OpenAI, Anthropic, Google, Meta, Thinking Machines and others, e.g. to test their models and collaborate on the science of scheming. 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. We also build a coding agent security product called Watcher that secures agent deployments in companies. Our goal is to reduce the probability of catastrophic incidents by securing coding agents, learning about their real-world risks, and publishing our research on how to build these control systems most effectively. 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 neither required nor encouraged. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, a take-home test (3 hours), 3 technical interviews, and a final interview with Marius (CEO). There are no leetcode-style general coding interviews. You may use AI tools on the take-home; we judge the result the way we'd judge any contributor's work, so you are responsible for the quality of everything you submit. If you want to prepare, we suggest building simple monitors for coding agents and running them on your own Claude Code / Cursor / Codex / etc. traffic. 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. 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.