Machine Learning Engineer

📋 Job Overview

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.

🏢 About Gray Swan

Gray Swan is on a mission to empower the world to use AI safely and securely. We evaluate AI models for the leading frontier labs along with building real-time threat detection and adaptive adversarial red teaming agents for teams deploying AI. We're a team of approximately 50 people, well-funded, growing quickly. Our work directly influences how the world deploys AI agents and systems at scale.

🎯 The Role

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.

✅ Key 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• Experience with modern ML methods such as LLMs (training, finetuning, and/or analyzing), synthetic data generation pipelines, and AI safety or security work.
• Core Technical Skills: 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.

🎁 Benefits

• Salary: $160-257k, depending on level, plus performance-based bonus
• Equity: Competitive equity package
• Benefits: 401k with up to 4% matching, 28 days annual leave (vacation + holidays), Health, dental, and vision coverage, Catered lunches (Pittsburgh office), Flexible work arrangements, Visa sponsorship available for exceptional candidates

🛂 Visa & Eligibility

• Visa sponsorship available for exceptional candidates