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.
Learn more about how we work grayswan.ai.
THE ROLE
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.
Join Gray Swan AI to work alongside leading minds in AI safety and apply your technical depth to problems that genuinely matter!
WHAT YOU’LL DO
- 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.
WHO YOU ARE
EDUCATION
- Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.
EXPERIENCE
- 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.
- 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.
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.
BONUS POINTS IF YOU HAVE
- 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.
YOU’LL THRIVE HERE IF
- You are genuinely excited by the intersection of research and engineering, and want to both develop new AI safety ideas and see them running in real systems.
- You are motivated by real-world impact and want your work to directly influence how major AI companies deploy models right now (we work with many of the leading AI labs).
- You are eager to deepen your AI safety expertise by working alongside a team that includes some of the most respected and influential thinkers in the field.
- You thrive in a fast-paced, dynamic startup environment where ambiguity is expected.
- You bring strong collaboration and problem-solving skills, with a focus on driving meaningful, lasting impact.
COMPENSATION & BENEFITS
We offer a competitive compensation package designed to reward impact and incentivize growth. Our compensation philosophy is informed by our current valuation and recent industry data.
Salary: $160-257k, depending on level, plus performance-based bonus and a 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
TYPICAL INTERVIEW PROCESS
🔎 Application review. We read everything; we’ll respond within 10 days.
🗣 Recruiter Screen. We learn about you; you learn about us.
🧑💻 Technical interview or Hiring Manager Interview.
💻 Role related assesment
🗣 Experience & culture interview
😇 Reference checks. We’ll reach out to 3-5 references that you provide.
📃 Offer. If it’s mutual, we move fast.
Requirements
Must-haves
As a Machine Learning Engineer at Gray Swan AI, you will play a pivotal role in shaping the future of AI safety solutions.
Join Gray Swan AI to work alongside leading minds in AI safety and apply your technical depth to problems that genuinely matter!
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.
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.
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.
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.
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.
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.
You are genuinely excited by the intersection of research and engineering, and want to both develop new AI safety ideas and see them running in real systems.
You are motivated by real-world impact and want your work to directly influence how major AI companies deploy models right now (we work with many of the leading AI labs).
You are eager to deepen your AI safety expertise by working alongside a team that includes some of the most respected and influential thinkers in the field.
You thrive in a fast-paced, dynamic startup environment where ambiguity is expected.
Nice-to-haves
Strong programming experience in Python and C++ (preferred)
Application prep
Use the role’s language and application details while tailoring your materials.