AI-Native Product Engineer - Manager
$150,700 per year
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L6
$150,700 per year
- Start with the user. Work directly with users and domain experts to understand real workflows, observe friction, and identify where AI can create distinctive value.
- Test assumptions quickly. Turn product hypotheses into concise specifications, prototypes, and small releases that generate useful evidence early.
- Own complete product slices. Build coherent verticals across the experience, services, data, AI behavior, and enterprise integrations rather than optimizing a single layer.
- Shape AI behavior. Design instructions, context, retrieval, structured outputs, and tool use, choosing adaptation or tuning only when evidence supports it.
- Build the evidence system. Create representative tasks and automated evaluations that show whether the product is useful, correct, safe, accessible, and improving.
- Connect outcomes to behavior. Instrument user journeys so the team can relate adoption and task success to system behavior, latency, failures, and cost.
- Engineer for responsible use. Build clear permission boundaries, secure data handling, human checkpoints, graceful degradation, recovery, and rollback into the experience.
- Own production quality. Release incrementally, monitor live behavior, respond to incidents, and turn failures into durable product and engineering improvements.
- Make practical trade-offs. Balance experience, quality, speed, reliability, and cost, using deterministic software when it is the more dependable choice.
- Contribute reusable patterns. Share proven components and evaluation assets with the broader platform while remaining accountable for their performance in the product workflow.
- Skills And Attributes For Success
- Full-stack engineering: Strong ability to build maintainable production software across modern interfaces, services, APIs, data, and integrations.
- AI application depth: Practical knowledge of model APIs, context design, retrieval, tool use, structured outputs, and agentic patterns.
- Product judgment: Ability to find the real user problem, frame clear hypotheses, and simplify the solution when evidence challenges the original idea.
- Evaluation discipline: Experience designing representative tests and experiments for systems whose behavior is not fully deterministic.
- Production ownership: Strong instincts for observability, accessibility, security, performance, reliability, recovery, and responsible use.
- High-agency collaboration: Comfort working across product, design, domain, platform, and control teams while staying accountable for the outcome after launch.
- To qualify you must have 7+ years of relevant and applicable experience
- Bachelor’s Degree in a relevant field of study
- End-to-end delivery: A record of taking customer- or employee-facing software from an unclear concept through launch, learning, and adoption.
- Technical breadth: Strong full-stack capability across modern frontend, backend, API, data, and cloud technologies, demonstrated through shipped work.
- Applied AI experience: Hands-on experience building products with large language models, retrieval, tools, or comparable AI application patterns.
- Evaluation experience: Evidence of automated testing and behavioral evaluation for systems with variable or probabilistic outputs.
- Live-service ownership: Demonstrated responsibility for telemetry, debugging, reliability, incident response, recovery, and performance improvement.
- User-centered practice: Experience working directly with users, designers, and product leaders to turn evidence into better product decisions.
- Engineering craft: A consistent record of clear specifications, maintainable code, thoughtful review, and sound technical trade-offs.
- This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity.
- Ideally, you’ll also have
- Agentic products: Experience building workflow automation or collaborative AI products that interact with real business systems.
- Enterprise environments: Experience with identity, knowledge systems, permission-sensitive information, or regulated and confidential settings.
- Design sensibility: A strong instinct for clear interactions, accessibility, and reducing user effort in complex workflows.
- Efficiency at scale: Experience improving the quality, latency, reliability, or economics of production AI applications.
- Builder track record: Startup experience, open-source contributions, or another clear example of high-agency product building.
- We welcome exceptional candidates from product, service design, operations, transformation, policy, domain, and entrepreneurial backgrounds; no particular degree or certification is required.
- What We Offer You
Level
LeadL6
Salary
$150,700 per year
Location
Jacksonville, FL
Occupation
Software Developers
Industry
Software Publishers
Posted
today
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