Staff Software Engineer, AI-Native Systems

Accountabilities Own the technical direction for a significant AI-native domain, such as agent architecture, platform abstractions, or evaluation and guardrail infrastructure.

Serve as technical lead for a squad or cross-team initiative by decomposing ambiguous problems, sequencing delivery, removing blockers, and keeping teams focused on measurable outcomes.

Lead architecture and design decisions, write and review design documentation, and establish clear technical ownership across complex initiatives.

Design and build production AI agents incorporating retrieval, orchestration, policy-based routing, tool/function calling, evaluation frameworks, and lifecycle observability.

Define standards for production-ready AI agents, including testability, rollback safety, cost controls, failure-mode management, and appropriate human-in-the-loop boundaries.

Build and extend shared AI platform abstractions, libraries, engineering patterns, and guardrails that enable teams to integrate AI capabilities safely and consistently.

Translate privacy, security, and regulatory requirements for sensitive data into practical technical architectures and engineering controls.

Deliver full-stack systems using TypeScript/Node.js and Python, including services, APIs, data-processing workflows, and internal interfaces.

Apply cloud-native infrastructure, event-driven architecture, CI/CD, monitoring, and observability practices to create scalable and reliable systems.

Own production deployment, monitoring, troubleshooting, and on-call responsibilities while continuously improving the operational health of inherited systems.

Partner with product, operations, clinical operations, and business leaders to identify valuable AI use cases, challenge low-value initiatives, and influence roadmap priorities.

Lead design sessions, proofs of concept, and collaborative build sessions to drive adoption and establish trust with internal users.

Define evaluation strategies and metrics covering agent accuracy, latency, safety, reliability, and cost-effectiveness.

Instrument AI systems so their behavior can be understood and evaluated beyond demonstrations, using data and feedback to continuously improve performance.

Mentor engineers through code reviews, design reviews, pairing, and direct feedback while creating reusable documentation, patterns, and best practices.

Build and strengthen the internal engineering community around AI-native and agentic development.

Within the first 90 days, develop a clear understanding of the AI platform, ship a meaningful contribution, and identify areas of high technical leverage.

Within six months, take ownership of a domain, lead technical direction within it, and establish a robust evaluation approach for deployed agents.

Within the first year, drive adoption of reusable patterns across teams and become a recognized technical multiplier for the broader engineering organization.

Requirements

8+ years of experience building and operating production software, with significant full-stack depth across TypeScript/Node.js and another programming language, preferably Python.

Proven technical leadership as an individual contributor, including domain ownership, leading multi-engineer initiatives, and influencing decisions across team boundaries without relying on formal authority.

Hands-on experience designing, deploying, and operating agentic systems in production, including retrieval, orchestration, tool/function calling, and evaluation.

Strong understanding of the practical strengths and limitations of LLMs and AI agents, with the judgment to determine where they provide genuine business value.

Demonstrated ability to take loosely defined problems from initial discovery through implementation, deployment, measurement, and iteration.

Experience using AI-powered development tools to accelerate engineering work, with sound judgment about when to trust, validate, or override AI-generated output.

Strong cloud-native engineering fundamentals, including CI/CD, observability, production operations, and system reliability.

Fluency with relational databases and SQL.

Strong written and verbal communication skills, including the ability to produce compelling technical design documents and communicate complex trade-offs to technical and executive audiences.

Comfort working remotely across functions and teams, with strong collaboration and stakeholder-management capabilities.

A high degree of autonomy, comfort with ambiguity, and a bias toward shipping measurable results.

Experience with production-scale agent frameworks or multi-agent architectures is strongly desired.

Background in model evaluation, guardrails, regression detection, and AI safety infrastructure is a plus.

Experience building platform capabilities used by multiple engineering teams is highly desirable.

Experience with workflow automation, forecasting-oriented products, or supply-and-demand matching systems is beneficial.

Experience working in a regulated environment such as healthcare/HIPAA or financial services, particularly involving sensitive data and AI, is strongly preferred.

Prior mentoring or formal technical-lead experience is advantageous.

Benefits

Salary range: $185,725–$264,500 USD annually.

Remote-first work model for US-based employees.

Medical, dental, and vision coverage.

Life insurance and short- and long-term disability coverage.

401(k) plan with company matching.

Flexible paid time off.

Paid parental leave.

Stock options.

Additional employee programs and perks supporting financial security, health, and well-being.

Opportunity to work in an AI-forward engineering environment where AI is integrated into both development practices and products.

Significant technical ownership and the opportunity to influence architecture, engineering standards, and organization-wide practices.

Inclusive workplace committed to equal employment opportunity and diverse perspectives.

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