Modern Relay is building the knowledge platform for the agent era. Our product caters a new kind of company: one in which humans work alongside internal and external AI agents, and where coordination, context and trust become critical infrastructure. The platform provides a shared layer of truth where both humans and agents can propose updates, contribute knowledge and trigger workflows. This result in a living, compounding knowledge hub that can be read from, written to and improved by both people and software.
Role OverviewWe’re looking for a Principal Engineer to lead the design and delivery of core platform capabilities across our stack. You’ll operate as a senior technical leader—owning architecture decisions, driving execution on high-impact projects, and raising the bar for reliability, security, and developer velocity. This role is ideal for someone who thrives in ambiguity, can translate product goals into scalable systems, and enjoys mentoring engineers while staying hands-on.
LocationsSan Francisco, CA
New York City, NY
Barcelona, Spain
Europe (remote)
United States (remote)
Own end-to-end architecture for key parts of the Modern Relay platform, from concept through production operations
Design and implement scalable systems that power agentic collaboration, including orchestration, integrations, and data/knowledge layers
Lead technical strategy for applying AI techniques (e.g., retrieval, evaluation, agent tooling) to deliver reliable, measurable product outcomes
Build and evolve knowledge representations (namely knowledge graphs) to improve reasoning, personalization, and system observability
Drive engineering excellence: testing strategy, performance tuning, reliability practices, security reviews, and incident learnings
Partner closely with Product, Growth, and customer-facing teams to translate requirements into robust technical solutions
Provide technical support and expertise in customer-facing contexts (troubleshooting, implementation support, and technical guidance)
Mentor and unblock engineers through design reviews, pairing, and setting clear technical standards
Identify and execute on high-leverage improvements to developer experience, tooling, and platform foundations
Perform other tasks and duties necessary for the proper fulfillment of the role and the Company’s business needs
Core systems are measurably more reliable, secure, and scalable, with clear SLOs/SLAs and strong operational hygiene
Architecture decisions are well-documented, pragmatic, and enable faster product iteration without sacrificing quality
AI/knowledge features ship with strong evaluation, monitoring, and clear feedback loops to improve performance over time
Cross-functional teams trust engineering as a partner—requirements are clarified early and delivered predictably
Engineers are unblocked and growing through consistent technical leadership and high-quality reviews
4–7 years of professional software engineering experience, including ownership of production systems
Strong system architecture skills: designing distributed systems, APIs, data models, and integration patterns
Experience building with or alongside AI systems in production (e.g., LLM applications, retrieval systems, evaluation/monitoring, agent frameworks)
Familiarity with knowledge graphs or graph-based modeling (or strong interest and ability to ramp quickly)
High technical bar: ability to write and review high-quality code, and to make sound tradeoffs under constraints
Proven technical leadership: driving projects, aligning stakeholders, mentoring engineers, and setting standards
Comfort engaging in customer-facing technical work when needed (debugging, implementation support, technical explanations)
Strong communication skills—able to explain complex systems clearly to both technical and non-technical audiences
System architecture
Artificial intelligence (production AI/LLM systems)
Knowledge graphs / graph modeling
Technical leadership
Cross-functional collaboration
Build core AI infrastructure that directly impacts product reliability and customer outcomes
Work on real-world agent coordination problems where data quality, structure, and evaluation matter as much as models
High autonomy and ownership in a fast-moving team shipping at the frontier of applied AI
A chance to define how Modern Relay’s agents learn from data and improve over time
