Applied Control Researcher
Apollo Research · Added today
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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.
Systematically collect and catalog coding agent failure modes from real-world instances, our internal deployments, public examples, research literature, and theoretical predictions
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
Stay current with research on AI safety, agent failures, and detection methodologies
Stay current with research into coding security and safety vulnerabilities
Develop & maintain a comprehensive library of monitoring prompts tailored to specific failure modes (e.g., security vulnerabilities, goal misalignment, deceptive behaviors)
Experiment with different reasoning strategies and output formats to improve monitor reliability
Design and test hierarchical monitoring architectures and ensemble approaches
Optimize log pre-processing pipelines to extract relevant signals while minimizing latency and computational costs
Implement and evaluate different scaffolding approaches for monitors, including chain-of-thought reasoning, structured outputs, and multi-step verification
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
Build automated red-teaming pipelines that attack monitors at scale
Design iterative adversarial games where a red-team and blue team continuously attack and defend respectively
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
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
Build a comprehensive failure mode database: Systematically collect and categorize 100+ distinct AI agent failure modes across safety and security dimensions, creating the foundation for our monitoring library.
Develop and validate monitoring approaches: Create and empirically test monitoring prompts and strategies for key failure categories, establishing clear metrics for monitor performance and building evaluation frameworks to track progress.
Optimize the monitoring pipeline: Improve log preprocessing and monitor scaffolding to achieve measurable improvements in detection accuracy, false positive rates, and computational efficiency.
Advance monitoring capabilities: Work on advanced approaches such as fine-tuned monitors or agentic investigation systems
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.
This role offers market competitive salary, equity, and competitive benefits.
Salary: 100k - 200 GBP (~150k - 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
Relocation support and visa fees (if applicable)
Time Allocation: Full-time
Location: This is an in-person role working out of our London or San Francisco 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.
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