Staff AI Enablement Engineer

Accountabilities

• Serve as the engineering subject-matter expert for generative AI, agentic systems, AI-assisted development, and automation, staying current with rapidly evolving technologies and distinguishing durable capabilities from emerging hype.
• Meet with engineering teams to understand workflows, identify high-friction areas, and translate findings into a prioritized backlog of AI-enabled solutions.
• Own the AI enablement roadmap from strategy and leadership alignment through implementation, measurement, iteration, and communication of results.
• Evaluate, pilot, and guide adoption of AI coding assistants and agentic developer tools based on demonstrated value, organizational readiness, technical fit, cost, quality, and risk.
• Design, build, and operate a scalable network of specialized automation agents addressing high-friction engineering and operational workflows.
• Establish shared architectures and reusable patterns that allow new AI agents and automations to be developed and deployed efficiently and reliably.
• Instrument AI solutions and measure outcomes such as time saved, quality improvements, adoption, and operational efficiency, using real-world usage data to drive continuous improvement.
• Partner with teams across the organization to modernize rules-based and heuristic systems into more data-driven and ML-informed solutions.
• Collaborate with data and domain stakeholders to determine appropriate approaches to features, evaluation, model selection, and production implementation.
• Define practical AI adoption frameworks and guardrails covering quality, security, cost, responsible use, and operational reliability.
• Promote AI and automation best practices through documentation, demonstrations, workshops, brown-bag sessions, and hands-on collaboration with engineering teams.
• Clearly communicate technical tradeoffs—including build versus buy, cost, latency, quality, scalability, and risk—to both engineering teams and senior leadership.
• Act as a technical force multiplier by helping teams integrate effective AI practices into their everyday development workflows.

Requirements

• 8+ years of professional software engineering experience, with a demonstrated history of designing, building, and operating production systems end to end.
• Hands-on experience building production applications with large language models and agentic systems, including prompt engineering, context and tool design, evaluation, orchestration, and operationalization.
• Production-level experience using modern AI coding assistants and agentic development tools, combined with a clear perspective on when and how these technologies provide meaningful value.
• Working knowledge of applied machine learning fundamentals, including features, evaluation, model selection, and the transition from heuristic approaches to ML-based systems.
• Ability to drive technical adoption across an engineering organization without relying on formal authority, using credibility, communication, collaboration, and working software to influence change.
• Strong product instincts for internal developer tooling, including the ability to engage directly with engineering users, identify high-value problems, prioritize effectively, and iterate quickly.
• Excellent written and verbal communication skills, with confidence presenting roadmaps, technical tradeoffs, recommendations, and measurable results to engineering leadership.
• Previous experience operating at staff or principal individual-contributor level, ideally with significant cross-team, platform, or organizational scope.
• Experience building internal AI enablement programs, developer-experience platforms, or lightweight AI governance frameworks is a plus.
• Experience in healthcare, health technology, or another regulated industry is preferred.
• Familiarity with vector search, retrieval-augmented generation (RAG), model evaluation frameworks, and observability for LLM-based systems is advantageous.
• Experience with conversational AI or virtual assistant products is a plus.
• Strong ownership, curiosity, adaptability, and judgment, with the ability to work effectively in a fast-moving environment where priorities evolve.

Benefits

• Base compensation: $190,000–$230,000.
• Health coverage: Medical, dental, and vision benefits effective from day one.
• Retirement: 401(k) plan with employer matching.
• Time off: Generous paid time off and paid holidays.
• Family support: Paid parental leave.
• Remote flexibility: Remote-first work environment with flexibility to work from home.
• Professional development: Access to learning resources and opportunities to build and expand your skills.
• Mission-driven work: Opportunity to apply AI and engineering expertise to meaningful challenges in mental healthcare and patient experience.
• High-impact environment: Direct exposure to engineering leadership and the opportunity to shape AI adoption across an organization.
• Inclusive culture: Commitment to a diverse, equitable, and inclusive workplace where employees are encouraged to bring their perspectives and experiences.
• U.S. employment: Candidates must be authorized to work in the United States; employment authorization is verified through the applicable employment eligibility process.