Backend Software Engineer (Research team) in London

Overview

We are seeking 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

Responsibilities

• Rapidly prototype and iterate on internal tools and libraries for building and running frontier language model evaluations
• Lead the development of major features from ideation to implementation
• Collaboratively define and shape the software roadmap and priorities
• Establish and advocate for good software design practices, codebase health, and coding agent practices
• Work closely with researchers to understand what challenges they face
• Assist researchers with implementation and debugging of research code
• Communicate clearly about technical decisions and tradeoffs
• You must have experience writing production-quality python code
• We value candidates from diverse backgrounds and recognise that candidates may demonstrate their skills in different ways

Qualifications / Nice-to-have

• Led the development of a successful software tool or product over an extended period (e. g. 1 year or more)
• Started and built the tech stack for a company, e. g in a start-up
• Worked your way up in a large organisation, repeatedly gaining more responsibility and influencing a large part of the codebase
• Authored and/or maintained a popular open-source tool or library
• Placed in a prestigious programming competition (IOI, ICPC, etc.)
• 5+ years of professional software engineering experience
• Experience working with LLM agents or LLM evaluations
• Infosecurity / cybersecurity experience
• Experience working with AWS
• Interest in AI Safety
• 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

• This role offers market competitive salary, equity, and competitive benefits.
• Salary: 100k - 200k GBP (~135k - 270k USD)
• 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
• About Apollo Research

The rapid rise in AI capabilities offer tremendous opportunities, but also present significant risks.

Apollo Research is primarily concerned with risks from Loss of Control, i. e. risks coming from the model itself rather than humans misusing the AI.

We’re 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 to test their models before deployment or collaborate on scheming mitigations.

We aim for a culture that emphasizes truth-seeking, goal-orientation, constructive feedback, and being friendly and helpful.

More details about working at Apollo can be found here.

About the Team

The SWE team currently consists of specific individuals.

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.

Our multi-stage process includes a screening interview, a take-home test (approx. 2 hours), 3 technical interviews, and a final interview.

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, such as building LM agent evaluations in Inspect.

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