Head of Engineering

Remote (United States)

Job Details

Location: United States

Workplace: Remote

Core Areas: Technical strategy, platform architecture, engineering leadership, production AI/ML systems, security and reliability, delivery execution, infrastructure economics, executive partnership

Compensation: $228,000–$240,000 base salary per year plus early-stage equity

About the Role

This opportunity is for a Head of Engineering who will own technical strategy, platform architecture, and the engineering organization behind a production AI platform. The role is responsible for translating company strategy into technical direction, scaling architecture and engineering operations, building the engineering organization, and ensuring ambitious product commitments can be delivered reliably.

The position combines hands-on technical leadership with executive-level ownership. It requires deep experience with large-scale systems, production AI and ML, distributed infrastructure, security, privacy, reliability, engineering delivery, and cost management. The Head of Engineering will work closely with executive leadership, Product, Research, Applied AI, and Security while providing clear technical risk and opportunity assessments to leadership and the board.

What You'll Do

• Own the technical strategy that translates company goals into a scalable, dependable platform.
• Establish a clear technical point of view on the direction of AI and determine how the platform should evolve in response.
• Make build, buy, and strategic technology decisions that shape the platform over the next several years.

• Own the architecture of the core collective-intelligence platform and its neuro-symbolic systems.
• Ensure the architecture can support growth, additional professional communities, increasing reasoning demands, and higher trust requirements.
• Balance near-term delivery needs with durable architectural foundations.

• Build, structure, and lead the engineering organization, including hiring, leveling, and leadership development.
• Set and maintain a high standard for engineering craft and technical quality.
• Create an environment where strong engineers can perform at a high level, grow, and do meaningful work.

• Own how the engineering organization plans, ships, learns, and improves while maintaining speed and trust.
• Drive delivery through defined milestones with clear accountability.
• Identify and remove organizational and technical blockers that slow engineering execution.

• Own security, privacy, and reliability for a platform handling sensitive community knowledge.
• Make trust a property of the architecture rather than a final-stage review step.
• Remain accountable for platform behavior, resilience, and reliability in critical situations.

• Partner with Research and Applied AI to translate frontier AI capabilities into dependable production behavior.
• Determine which AI advances are ready for production and which require further validation.
• Maintain clear technical judgment about what AI can and cannot reliably do.

• Serve as a senior technical leader and translate between business strategy and technical reality.
• Provide leadership and the board with a clear, evidence-based view of technical risks and opportunities.
• Partner with Product to determine what can be built and establish realistic delivery expectations.

• Own the economics of operating the platform, including inference and infrastructure costs at scale.
• Build the operational discipline required to keep a growing platform reliable and financially sustainable.
• Plan for scale proactively instead of treating capacity and infrastructure growth as recurring emergencies.

• Set the standard for using AI to improve engineering velocity and quality across coding, review, and operations.
• Ensure human judgment, interpretation, and accountability remain central to engineering decisions.
• Promote evidence-based decision-making rather than measuring value purely through output volume.

Qualifications

Required Experience

• Extensive engineering leadership experience, including leading and scaling engineering organizations.
• Deep technical experience with large-scale systems and sufficient hands-on credibility to earn the trust of senior engineers.
• Direct experience building and operating AI or ML systems in production.
• Demonstrated ownership of platform architecture that scaled alongside a growing business.
• Strong judgment regarding security, privacy, and reliability for sensitive data.
• Executive presence and the ability to translate effectively between business strategy and technical strategy.
• Proven success attracting, developing, and retaining strong engineers.
• Prior senior engineering leadership experience such as VP Engineering, CTO, or an equivalent role within a software or AI organization.
• Experience scaling both technical architecture and engineering organizations through periods of growth.
• A track record of shipping and operating production AI systems.
• Experience partnering with executive leadership and boards.

Required Skills

• Ability to define technical strategy and make architecture-level decisions for large-scale platforms.
• Strong capability in building, structuring, and scaling engineering organizations.
• Practical expertise with production AI and ML systems, including reliability, infrastructure, and cost considerations.
• Strong understanding of security, privacy, governance, and trust requirements for sensitive data.
• Ability to lead engineering delivery, establish operational discipline, and maintain execution quality at scale.
• Strong executive communication skills, including the ability to communicate technical risk and opportunity at board level.
• Ability to translate research and frontier AI capabilities into dependable production systems.
• Ability to make difficult technical and organizational decisions, defend them with evidence, and remain accountable for outcomes.
• Ability to maintain high standards for security, reliability, trust, engineering craft, and team performance.

Preferred Qualifications

• Experience with neuro-symbolic systems, knowledge graphs, retrieval systems, or agentic AI.
• Experience managing inference and infrastructure economics at scale.
• Experience working in trust-sensitive or regulated domains.
• Familiarity with CRM, AMS, or knowledge-platform ecosystems.
• Experience as a technical co-founder or early-stage engineering leader.

Services and Tools Experience

• Experience with modern cloud platforms such as AWS, GCP, or Azure and with large-scale infrastructure.
• Experience with distributed systems, data platforms, and ML infrastructure.
• Architectural-level familiarity with LLM, retrieval, vector, and graph systems.
• Experience with observability, security, and reliability tooling.
• Experience with engineering delivery and planning systems.
• Experience managing cost and capacity for AI workloads.

Benefits

• Generous health and wellness benefits.