AI Safety Researcher

3+ years of experience in AI/ML research, with a focus on safety, robustness, and interpretability Strong background in machine learning theory, with practical experience implementing models and algorithms Expertise in AI safety frameworks, fault tolerance, and risk mitigation strategies for AI systems Experience with reinforcement learning, adversarial training, and robustness testing of AI models Proficiency in programming languages such as Python, C++, or Go, with hands-on experience in AI development libraries (e.g., TensorFlow, PyTorch) Strong understanding of AI ethics, fairness, and the impact of machine learning algorithms in real-world applications Ability to identify potential safety risks in AI-driven systems and design solutions to address them Familiarity with distributed systems, cloud infrastructure, and build/test automation frameworks

Conduct research into AI safety, focusing on robustness, fairness, and interpretability of machine learning models used in build systems Develop algorithms and frameworks that ensure the safe deployment of AI-powered automation in software build, testing, and CI/CD workflows Work closely with engineering teams to integrate AI safety mechanisms and ensure robust error handling and fault tolerance Investigate and mitigate risks associated with AI-driven decision-making in distributed build systems Contribute to the development of safety-critical AI models for optimizing performance, caching accuracy, and task coordination Conduct studies on the ethical implications of AI in software development Perform in-depth testing, model validation, and risk assessment to ensure AI systems meet reliability and safety standards Collaborate with product managers and engineers to translate research findings into practical tools and features