Applications deadline: We're accepting applications until 30 April 2026. We encourage early submissions.
Apollo Research is a rapidly growing AI safety and model evaluation company, providing third-party evaluations for frontier AI models. The capabilities of current AI systems are evolving rapidly. While these advancements offer tremendous opportunities, they also present significant risks, such as the potential for deliberate misuse or the deployment of sophisticated yet misaligned models. At Apollo Research, our primary concern lies with deceptive alignment, a phenomenon where a model appears aligned but is, in fact, misaligned and capable of evading human oversight.
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
You’ll be Apollo’s second Finance hire. You’ll work with our Senior Finance Manager to execute the day-to-day engine that keeps Apollo running and convert business activity into consistent, trusted numbers for management and investors.
This role starts part-time, about 1.5 to 2 days a week, in person in London. As we double in size over the next 12 months and you show your strengths, your responsibilities and days will increase, with a clear path to a full-time position. This isn’t a side gig; we're looking for someone who wants to grow with us.
As our finance team develops, you’ll have the chance to focus on Controllership or FP&A, taking on more responsibility based on your interests and strengths.
Apollo Research is an AI safety organization. We specialize in auditing high-risk failure modes, particularly deceptive alignment, in large AI models. Our primary objective is to minimize catastrophic risks associated with advanced AI systems that may exhibit deceptive behavior, where misaligned models appear aligned in order to pursue their own objectives.
Our approach involves conducting fundamental research on interpretability and behavioral model evaluations, which we then use to audit real-world models. Ultimately, our goal is to leverage interpretability tools for model evaluations, as we believe that examining model internals in combination with behavioral evaluations offers stronger safety assurances compared to behavioral evaluations alone.
