About the job
As a Machine Learning Engineer at Gray Swan AI, you will play a pivotal role in shaping the future of AI safety solutions. Research at Gray Swan AI is tightly tied to real-world impact. AI security is not a solved problem, and this role is a mix of applied research and system building: developing new approaches to adversarial testing, model evaluation, and robust inference that directly inform how secure AI systems are deployed in practice. You will work at the boundary between research and production, translating novel ideas into scalable AI systems that withstand adversarial pressure. Your expertise in state-of-the-art deep learning architectures, distributed systems, and parallel computing will enable you to tackle complex challenges associated with resource-intensive models. You will be responsible for advancing our methodologies for controlling, monitoring, and analyzing these models, ensuring they meet the rigorous demands of production environments.
Responsibilities
- Lead the design, development, and deployment of advanced machine learning models to enhance system performance and scalability.
- Tackle complex challenges associated with resource-intensive models using distributed systems and parallel computing.
- Advance methodologies for controlling, monitoring, and analyzing machine learning models in production environments.
- Develop new approaches to adversarial testing, model evaluation, and robust inference.
- Translate research ideas into scalable AI systems deployed in real-world, adversarial settings.
- Work closely with cross-functional teams to ensure research outcomes inform production systems.
Qualifications
Minimum
- Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.
- Experience in building and deploying machine learning models and systems.
- Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
- Strong programming experience in Python and C++ (preferred)
- Practical experience developing scalable machine learning pipelines and integrating them with cloud infrastructure (e.g., AWS, GCP, Azure).
- Experience conducting ML research, including building research prototype systems, experiment design, empirical analysis of results, and communicating results via publications.
Preferred
- Experience with AI safety practices such as model validation, robustness testing, and continuous monitoring for safety and security incidents throughout deployment.
- Experience with AI safety and security assessments and adversarial testing.