About the job
As a Senior 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.
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.
- Mentor junior engineers and drive innovation within the team.
- 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.
- 6+ years of hands-on experience in building and deploying machine learning models and systems.
- Experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing), synthetic data generation pipelines, and AI safety or security work.
- Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
- 6+ years of experience programming 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
- Technical leadership experience, including leading project teams or mentoring junior engineers.
- Experience with AI safety practices such as model validation, robustness testing, and continuous monitoring for safety and security incidents throughout deployment.
- Familiarity with AI safety and security assessments and adversarial testing.
- Hands-on experience working in collaborative, cross-functional environments with data scientists, software engineers, and product managers.
- Strong communication skills for articulating complex technical concepts and influencing team strategies.