Application Guide
How to Apply for Forward Deployed Engineer – Machine Learning
at Gray Swan
🏢 About Gray Swan
Gray Swan is an AI security company focused on assessing risks in AI models, operating at the intersection of cutting-edge AI development and critical safety concerns. What makes them unique is their proactive approach to uncovering AI safety problems before they become widespread issues, positioning them as pioneers in a rapidly evolving field. Someone might want to work there to be at the forefront of AI safety research while building practical solutions for enterprise AI deployment.
About This Role
As a Forward Deployed Engineer – Machine Learning at Gray Swan, you'll stress-test the latest agentic AI systems in lab environments while helping enterprises deploy AI safely at scale. This role involves uncovering novel AI safety problems before they emerge in production and translating those insights into tangible products and playbooks. You'll have direct impact by working on the 'messiest' edge cases where AI systems behave unpredictably, making this position crucial for advancing both AI capabilities and safety simultaneously.
💡 A Day in the Life
A typical day might involve designing and running stress tests on new agentic AI models in Gray Swan's lab environment, analyzing unexpected behaviors and safety vulnerabilities. You'd likely collaborate with research teams to document findings and then work on translating those insights into practical playbooks or product features that help enterprise clients deploy AI more safely. The role balances hands-on experimentation with strategic thinking about how to anticipate and mitigate AI risks before they impact real-world systems.
🚀 Application Tools
🎯 Who Gray Swan Is Looking For
- Has hands-on experience stress-testing AI systems in controlled environments, not just theoretical knowledge of ML algorithms
- Demonstrates a research-first approach to problem-solving with examples of uncovering novel issues in AI systems
- Possesses practical experience with AI safety evaluations, particularly around agentic AI systems and their failure modes
- Can articulate specific challenges faced during real-world AI deployments and how they addressed safety concerns
📝 Tips for Applying to Gray Swan
Highlight specific projects where you stress-tested AI systems in lab settings, detailing your methodology and unexpected findings
Demonstrate your 'research-first mindset' by describing how you approach novel AI problems systematically, not just applying existing solutions
Include concrete examples of AI safety evaluations you've conducted, specifying the metrics and frameworks you used
Show how you've worked on 'messy' edge cases in AI deployment, emphasizing your comfort with uncertainty and complex problems
Tailor your resume to emphasize forward-deployed engineering experience where you bridged research insights with practical deployment solutions
✉️ What to Emphasize in Your Cover Letter
["Your experience with agentic AI systems and specific safety challenges you've encountered", "Examples of how you've turned research insights into practical solutions for AI deployment", "Your approach to working on the 'edge' of AI where problems are novel and solutions aren't well-defined", "Why Gray Swan's focus on proactive AI safety assessment aligns with your professional goals and experience"]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Study Gray Swan's published research or blog posts on AI safety to understand their specific approach and terminology
- → Research current trends in agentic AI systems and their known safety challenges to demonstrate domain knowledge
- → Understand the specific industries or use cases where Gray Swan's clients deploy AI at scale
- → Review any public information about Gray Swan's products or tools for AI risk assessment
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on theoretical ML knowledge without demonstrating hands-on experience with AI safety evaluations
- Presenting yourself as purely a researcher without showing ability to translate findings into practical deployment solutions
- Using generic AI safety terminology without specific examples of stress-testing or risk assessment methodologies
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!