Backend Software Engineer (Research team)
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.
KEY RESPONSIBILITIES
KEY REQUIREMENTS
For example, we might be impressed if you have:
The following would be a bonus:
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.
LOGISTICS
BENEFITS
ABOUT APOLLO RESEARCH
The rapid rise in AI capabilities offer tremendous opportunities, but also present 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), the science of scheming (e.g. model organisms), and scheming mitigations (e.g. anti-scheming, and control). We closely work with multiple frontier AI companies, e.g. to test their models before deployment or collaborate on scheming mitigations. 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
The SWE team currently consists of Rusheb Shah, Andrei Matveiakin, Alex Kedrik, and Glen Rodgers. Beyond the SWE team, you will closely interact with the research scientists and engineers as the primary user group of your tools. 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.
INTERVIEW PROCESS
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, a take-home test (approx. 2 hours), 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 working on hands-on LLM evals projects (e.g. as suggested in our starter guide), such as building LM agent evaluations in Inspect.