AI Safety Research Engineer (ML Research Implementation)
Oct 20, 2023
Research Roles (AI Governance and AI Safety)
An AI safety research engineer focuses both on the practical implementation of existing AI safety techniques as well as development support for researchers while researching new techniques, which are often elaborated by AI safety research scientists. This purview includes overseeing and contributing to scientific experiments, building technique-specific software infrastructure, developing benchmarks or tests, designing API interfaces, optimizing and improving safety-related algorithms, proposing new safety ideas, and refining safety tools. Note that this job sometimes overlaps with the role of AI safety research scientist, since it consists in part in implementing the findings of AI safety research scientists.
Create scalable implementations of machine learning algorithms and apply them to conduct scientific experiments on safety.
Contribute to shaping a safety research direction, collaborate on implementation strategy, analyze experimental outcomes, and help document the findings.
Develop software infrastructure for the safety research team.
Example projects: optimizing the throughput of a new attention mechanism; comparing the interpretability properties of two transformer variants; scaling a distributed training job to thousands of GPUs.
Depending on the organization, tasks on system robustness may be bundled into the responsibilities, such as enhancing the reliability of the cluster for large-scale tasks, or optimizing throughput and efficiency.
Why we think this job is impactful
AI safety research engineers are instrumental in guiding responsible AI development and preventing catastrophic risks as they help to identify, understand, and address potential hazards and vulnerabilities within AI systems. Their work is key in finding solutions to prevent unwanted behavior from AI systems and ensure that they act in an aligned way.
How Successif can help
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