Machine Learning Engineer

Gray Swan is a company that protects organizations from AI security threats by building real-time threat detection and adaptive defenses. As a Machine Learning Engineer, you will lead the design and deployment of advanced machine learning models, tackling complex challenges and translating research into scalable AI systems.

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

Requirements:

• 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
• In-depth knowledge of neural network architectures, including sequence models, transformers, and other state-of-the-art approaches
• Strong algorithmic problem-solving skills and comprehensive knowledge of ML theory and optimization techniques
• Proficiency in data preprocessing, transformation, and handling large-scale, multi-modal datasets
• Good to have: experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing), synthetic data generation pipelines, and AI safety or security work
• 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