Machine Learning Engineer

Role Overview

As a Machine Learning Engineer at Gray Swan AI, you will help shape AI safety solutions by combining applied research with production system building. You’ll develop methods for adversarial testing, model evaluation, and robust inference that directly inform how secure AI systems are deployed in real-world, adversarial settings.

Responsibilities

• Lead the design, development, and deployment of advanced ML models to improve performance and scalability
• Tackle challenges from resource-intensive models using distributed systems and parallel computing
• Advance controlling, monitoring, and analyzing ML models in production
• Develop new approaches to adversarial testing, model evaluation, and robust inference
• Translate research ideas into scalable systems used in real-world adversarial environments
• Collaborate with cross-functional teams to ensure research informs production systems

Requirements

• Bachelor’s degree in Computer Science, Machine Learning, Engineering, or related field
• Experience building and deploying ML models and systems
• Demonstrated expertise using deep learning frameworks, especially PyTorch
• Strong programming in Python and C++ (preferred)
• Experience developing scalable ML pipelines and integrating with cloud infrastructure (AWS/GCP/Azure)
• Conduct ML research: build prototypes, design experiments, analyze results, and communicate findings

Nice to Have

• Experience with modern ML methods such as LLMs (training, fine-tuning, and/or analysis)
• Synthetic data generation pipelines
• Experience in AI safety and security work (validation, robustness testing, continuous monitoring)
• Experience with AI safety/security assessments and adversarial testing

Skills

• Deep knowledge of neural network architectures (sequence models, transformers, and state-of-the-art approaches)
• Strong algorithmic problem-solving and ML theory/optimization
• Ability to preprocess and transform large-scale multi-modal datasets