Founding Engineer

This role involves hands-on technical work across the full stack of our product, from data ingestion and real-time inference pipelines to anomaly detection models and customer-facing integrations.

Specific Responsibilities

1. Behavioural Analysis Models: Design and implement machine learning models that analyse AI agent activity in real time, identifying patterns of normal and anomalous behaviour across customer deployments.
2. Anomaly Detection Systems: Build and refine detection systems that identify security threats, policy violations, and unexpected behaviours in autonomous AI systems, with a focus on minimising false positives in production environments.
3. Inference Pipeline Development: Architect and optimise low-latency inference pipelines capable of processing high volumes of event data from customer AI agents, ensuring sub-second response times for security alerts.
4. Platform Integration: Collaborate with the CTO and other engineers to integrate ML components into the core security platform, ensuring seamless operation with monitoring dashboards, alerting systems, and compliance reporting tools.
5. Customer Technical Engagement: Work directly with enterprise customers and design partners to understand their security requirements, translating business needs into technical specifications and implementation plans.
6. Architecture and Technical Leadership: Contribute to architectural decisions as a senior member of the founding team, helping to establish technical standards, development practices, and infrastructure choices that will scale with the business.
7. Compliance and Security Requirements: Ensure all systems meet the security and compliance standards required by customers in regulated industries, including data handling, audit logging, and access controls.

Technical Environment

• Languages: Python (primary), with potential use of Go or Rust for performance-critical components
• ML Frameworks: PyTorch, TensorFlow
• Infrastructure: AWS/GCP, containerised deployments (Docker, Kubernetes)
• Data Processing: Real-time streaming architectures, time-series databases
• Security: Familiarity with threat modelling, secure development practices, and compliance frameworks

Required Qualifications and Experience

• Degree in Computer Science, Machine Learning, or a related field (or equivalent practical experience)
• Minimum 5 years of experience designing and building production machine learning systems
• Strong proficiency in Python and modern ML frameworks (PyTorch or TensorFlow)
• Experience with real-time inference systems and low-latency data pipelines
• Demonstrated ability to work autonomously and take ownership of complex technical problems

Desirable Experience

• Understanding of security concepts and threat modelling
• Experience with distributed systems and cloud infrastructure (AWS/GCP)
• Familiarity with AI agent architectures and autonomous systems
• Prior experience in a startup or early-stage company environment