ML/Research Engineer, Safeguards
Build ML systems to detect and mitigate AI misuse, including classifiers for anomalous behavior, multi-exchange harm monitoring, and agentic safety evaluations. Requires 4+ years ML experience, Python proficiency, and research-to-deployment skills.
About the job
Responsibilities
- Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on
- Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts
- Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks
- Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse
You may be a good fit if you
- Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry
- Have proficiency in Python and experience building ML systems
- Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems
- Are worried about misuse risks of AI systems, and want to work to mitigate them
- Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders
Strong candidates may also have experience with
- Language modeling and transformers
- Building classifiers, anomaly detection systems, or behavioral ML
- Adversarial machine learning or red-teaming
- Interpretability or probes
- Reinforcement learning
- High-performance, large-scale ML systems
Annual Salary: $350,000—$500,000 USD
Skills
Python, Machine Learning, Transformers, Classifiers, Anomaly Detection, Adversarial Machine Learning, Red-Teaming, Interpretability, Reinforcement Learning, Large-Scale Ml Systems
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