
Apollo Research · London & San Francisco
Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable. ABOUT THE OPPORTUNITY We want to ...
Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable.
We want to develop 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.
Note: We are not hiring for interpretability roles.
A diverse range of skill sets will be required to drive our research agenda forward and we don’t expect any single candidate to fulfill all the characteristics below. That being said, a successful candidate likely displays excellence at one or several of the following:
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.
Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable. ABOUT THE OPPORTUNITY We’re looking for Research Engineers/Scientists for our pre-deployment team to work on Training-Run Assessments (TRAs). You will design and build automated pipelines for assessing whether egregious misalignment or scheming are emerging at any point of frontier post-training. This will involve evaluating and red-teaming of checkpoints at various stages of post-training as well as automated analysis of post-training data. You will get to work with frontier labs like OpenAI, Anthropic, and Google DeepMind and be among the first to interact with new models before anyone else. Our ideal candidate loves rigorously testing frontier AI models, and enjoys building efficient pipelines for automated analysis. KEY RESPONSIBILITIES Run and own pre-deployment engagements: We run a pre-deployment evaluation campaign with a frontier AI lab approximately every two weeks with thousands of runs across hundreds of distinct environments. We explore behaviors learned during training and check for undesirable behaviors like alignment faking, perform targeted follow-up experiments/red-teaming, and report our findings to the frontier AI lab we’re working with. Develop methodology for training-run assessments: in-between campaigns we improve our methodology, which might mean implementing new evals or building infrastructure for automated red-teaming. KEY REQUIREMENTS We don’t require a formal background or industry experience and welcome self-taught candidates. Software engineering skills: Our entire stack uses Python. We're looking for candidates with strong software engineering experience. Ideally, you have experience shipping and maintaining production Python code, and know how to factor messy problems into clean abstractions that others can use and extend. 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 and communication: You succinctly convey qualitative and quantitative findings to a technical and non-technical audience. 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. NICE TO HAVE Experience thinking about AI risk topics like scheming and metagaming. Knowledge of LLM post-training: topics like RLHF, reasoning training, supervised fine-tuning, on-policy distillation, etc. We are using Inspect as our primary evals framework, and we value experience creating evals with it or similar frameworks like Harbor. 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.
Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable. ABOUT THE OPPORTUNITY We’re looking for Backend Software Engineers who are excited to build tools for frontier AGI safety research, e.g. building and maintaining evals libraries and tools for monitoring and controlling our own LLM traffic. REPRESENTATIVE PROJECTS Here is a list of example projects which you might build and ship in your first 6 months. - Internal tooling for efficiently running and analyzing evaluations. For example, a tool that quickly investigates thousands of agentic eval runs in parallel and surfaces interesting information automatically - Automated evaluation pipelines to minimize the time from getting access to a new model for pre-deployment testing to analyzing the most important results and sharing them - Orchestration tools that allow researchers to run thousands of agentic evaluations in parallel on remote machines with high security and reliability - LLM proxy service that enables us to monitor all of our coding agent traffic in real time and identify undesired behavior automatically (in the spirit of Control) - LLM agents and MCP tools to automate internal software engineering and research tasks, with sandboxes to prevent major failures - CI pipeline optimisations to reduce execution time and eliminate flaky tests - Telemetry API and instrumentation of our existing tools, allowing us to monitor usage and improve reliability - Data warehousing pipeline and service to store thousands of eval transcripts which researchers can study and build datasets from - Upstream improvements to the Inspect framework and ecosystem, e.g. support for evaluating modern agentic scaffolds.
Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable. ABOUT THE OPPORTUNITY We’re looking for Full-stack Software Engineers who are excited to build tools for frontier AGI safety research, e.g. building and maintaining evals libraries and tools for monitoring and controlling our own LLM traffic. REPRESENTATIVE PROJECTS Your main objective is to develop tooling for analyzing model evaluation results. Here is a list of features that you might build and ship in your first 6 months: - LLM-powered search that finds interesting fragments in evaluation transcripts - Comparison views that show how conversations and scores differ between two evaluation runs - Ability to view and analyse conversations with coding agents (Cursor, Claude Code, etc.) in addition to evaluation transcripts - Results streaming for evaluations that are currently being run - Collaborative editing of evaluation logs that automatically updates metrics and other derived data. Think of this as developing an “IDE for evaluations”. Besides this, here are example auxiliary projects which you might do: - Automated evaluation pipelines to minimize the time from getting access to a new model for pre-deployment testing to analyzing the most important results and sharing them. - LLM agents and MCP tools to automate internal software engineering and research tasks, with sandboxes to prevent major failures - Telemetry API and instrumentation of our existing tools, allowing us to monitor usage and improve reliability - Upstream improvements to the Inspect framework and ecosystem, e.g. support for evaluating modern agentic scaffolds.