Staff Engineer

About the Role

We are seeking an experienced Staff Engineer to lead the architecture, design, and large scale deployment of the ContextSensor, a core component of the ContextFabric platform. The ContextSensor powers the capture and flow of organizational context into AI agents, enabling them to deliver more reliable, accurate, and business-aligned outcomes.

This role is central to achieving massive scale—100k+ installations across enterprise environments—and requires expertise in distributed systems, high-throughput data processing, and resilient service design. Because the ContextSensor incorporates AI models into its runtime, you will also play a key role in optimizing model execution on end user devices, ensuring performance, efficiency, and reliability.

The ideal candidate thrives at the intersection of systems engineering, AI runtime optimization, and practical scalability challenges. You should be comfortable making critical tradeoffs, ensuring efficiency at the endpoint, and working cross-functionally with product, platform, and customer teams to ensure seamless deployment and adoption across diverse enterprise environments.

Key Responsibilities

Architecture & Design Leadership

• Define the architecture and technical roadmap for the ContextSensor, balancing scalability, resilience, and ease of deployment.
• Drive innovation in distributed system design, large-scale telemetry capture, and enterprise-scale deployment strategies.
• Ensure security, compliance, and governance considerations are designed into the system from the start.
• Lead design reviews, ensure adherence to best practices, and provide deep technical mentorship across the engineering team.

Scalable Implementation & Deployment

AI Runtime & Resource Efficiency

Data Processing & Performance Engineering

Cross-Functional Collaboration

Continuous Improvement & Scaling Innovation

Qualifications

Required :

C#, WPF, Python, Electron, HTML / CSS.

Preferred :

Success Metrics

Reliable deployment of the ContextSensor across 100k+ organizational endpoints.

Low-latency, high-throughput contextual data capture and AI model execution at scale.

Optimized end-user resource consumption with minimal overhead.

Measured improvements in uptime, resilience, and fault tolerance in production environments.

Smooth enterprise deployments with reduced time-to-value and minimal operational friction.

Continuous architectural improvements that anticipate and solve scaling challenges.