Application Guide

How to Apply for Senior Product Security Engineer

at Phaidra

🏢 About Phaidra

Phaidra develops AI-driven control systems specifically for industrial efficiency, focusing on reducing energy waste and environmental impact. Unlike generic AI companies, they specialize in applying reinforcement learning to physical infrastructure, making their work directly impactful on sustainability goals. Their remote-first culture in the UK allows for flexibility while tackling cutting-edge problems at the intersection of AI safety and industrial operations.

About This Role

This Senior Product Security Engineer role focuses on securing autonomous AI agents within industrial control systems, specifically adapting security practices for reinforcement learning pipelines and agentic AI development. You'll be designing safety boundaries between AI models and physical hardware controls, making this role critical for preventing real-world operational risks in energy-intensive environments. Your work directly enables safe deployment of AI that controls physical infrastructure.

💡 A Day in the Life

You might start by reviewing threat models for new autonomous agent features with researchers, then design safety boundaries for agent-hardware interactions. Afternoon could involve implementing security controls in RL training pipelines or reviewing architecture for secure agent deployment. You'll regularly collaborate with the Agentic AI team to embed security into their iterative development process while ensuring compliance with industrial safety standards.

🚀 Application Tools

Generate Cover Letter

AI-powered, tailored for Phaidra

Interview Prep

Practice questions for this role

🎯 Who Phaidra Is Looking For

  • Has 5+ years in product/application security with specific experience securing autonomous decision-making systems or reinforcement learning pipelines
  • Demonstrates practical understanding of agentic AI security risks like goal misalignment, reward hacking, and insecure tool execution in autonomous agents
  • Possesses strong Python/Go programming skills combined with hands-on experience with GCP, Kubernetes, and agent frameworks or RL libraries
  • Can translate traditional security practices to the iterative, simulation-heavy development cycles characteristic of reinforcement learning projects

📝 Tips for Applying to Phaidra

1

Highlight specific experience with RL security challenges - mention concrete examples of securing training pipelines or preventing simulation manipulation

2

Demonstrate understanding of industrial control system security implications when combined with autonomous AI agents

3

Show how you've adapted security practices for iterative development cycles (like those in RL) rather than just traditional SDLC

4

Reference Phaidra's SAIDL (Secure AI/ML Development Lifecycle) framework and suggest how you'd implement it for agentic AI

5

Provide examples of designing safety boundaries between software systems and physical hardware controls

✉️ What to Emphasize in Your Cover Letter

['Your experience with reinforcement learning security specifically, not just general ML/AI security', 'Examples of securing autonomous agents or automated decision-making systems in production environments', "How you've partnered with researchers to model threats in novel technology areas", 'Your approach to designing secure-by-default architectures for systems controlling physical infrastructure']

Generate Cover Letter →

🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Phaidra's specific approach to reinforcement learning in industrial settings - review their technical blog and publications
  • → Industrial control system security standards and how they intersect with autonomous AI agents
  • → The company's sustainability mission and how their AI systems reduce energy waste in specific industries
  • → Current challenges in agentic AI safety research relevant to physical system control

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through how you would threat model an autonomous agent controlling industrial equipment
2 Describe your experience securing GCP and Kubernetes environments for AI/ML workloads
3 How would you prevent reward hacking in a reinforcement learning system controlling energy usage?
4 Discuss approaches to creating deterministic safety guardrails between AI models and hardware controls
5 Explain how you'd adapt traditional security practices for the iterative nature of RL development cycles
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Treating this as a generic application security role without addressing the specific agentic AI/RL security requirements
  • Focusing only on traditional web/application security without demonstrating understanding of AI/ML pipeline security
  • Failing to show how security practices need adaptation for reinforcement learning's unique development cycles

📅 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:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Phaidra!

← Back to Job Listing Apply Now →