Lead Systems Engineer

Job description

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

- Build and optimize services capable of supporting 100k+ installations in enterprise-scale environments.

- Lead development of deployment strategies for cloud-native and hybrid enterprise environments.

- Develop self-service tooling, automation, and monitoring to reduce friction in large rollouts.

- Ensure observability, monitoring, and diagnostic tools are embedded for reliable operation at scale.

AI Runtime & Resource Efficiency

- Incorporate AI models into runtime systems, ensuring efficient, low-latency inference.

- Optimize performance of client-side components running on end-user machines, with a focus on resource efficiency (CPU, memory, and power usage).

- Balance accuracy, throughput, and efficiency in model integration for diverse enterprise hardware and environments.

Data Processing & Performance Engineering

- Architect and optimize pipelines for capturing, normalizing, and transmitting contextual data with low latency and high reliability.

- Address large-scale data volume challenges, ensuring efficient ingestion and processing.

- Lead efforts to reduce time-to-value by improving throughput, resilience, and interoperability.

Cross-Functional Collaboration

- Partner with Product to align ContextSensor capabilities with customer requirements and enterprise vision.

- Work closely with Customer Success and Enablement to ensure successful rollout and ongoing adoption.

- Translate customer feedback into platform-level improvements and roadmap adjustments.

- Collaborate with Security and Infrastructure teams to meet enterprise IT standards for installation and operation.

Continuous Improvement & Scaling Innovation

- Anticipate scaling bottlenecks and proactively design for future growth.

- Evaluate and adopt new technologies, frameworks, and practices to keep ContextSensor ahead of demand.

- Contribute to internal technical enablement by documenting best practices, deployment playbooks, and scaling strategies.

Qualifications

Required:

- 10+ years of experience in Desktop Application Development, with 6+ years focused on distributed systems, large-scale services, or data-intensive platforms.

- Proven track record of designing and scaling a client application to tens of thousands of installations or more.

- Expertise in service scalability, high-throughput data pipelines, and resilient architecture.

- Strong programming and system design skills in C#, WPF, Python, Electron, HTML/CSS.

- Familiarity with AI models, inference runtimes, and optimization techniques.

- Experience with monitoring, observability, and performance optimization at scale.

- Strong communication skills to collaborate and work with customers.

- Leadership skills, with ability to influence cross-functional teams.

Preferred:

- Experience building AI or ML-adjacent infrastructure.

- Familiarity with enterprise IT integration (identity, security, compliance).

- Prior success scaling SaaS or hybrid platforms into 100k+ deployment environments.

- Contributions to open-source distributed systems, AI runtime, or infrastructure tooling.

- Experience in Mac OS APIs and programming with Objective C or Swift.

Success Metrics

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

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

- Efficiency: Optimized end-user resource consumption with minimal overhead.

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

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

- Innovation: Continuous architectural improvements that anticipate and solve scaling challenges.

Required Skill Profession

Computer Occupations