Senior AI Software Engineer, Product

New

Based in United States, U.S. time zonesFull-TimeSenior

Salary$154,000 to $237,000 base salary

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Job Details

2+ years of professional software engineering experience with ownership of production systems from design through launch and iteration.
Demonstrated experience building and shipping AI-powered products with measurable impact on user or business outcomes.
Hands-on experience with large language models, including prompt engineering, retrieval-augmented generation (RAG), evaluation frameworks, and production monitoring.
Strong full-stack engineering skills with experience in backend systems (e.g., Ruby on Rails, Python, Go, or Node.js) and relational databases.
Experience building or contributing to modern front-end applications, ideally using React or similar frameworks.
Strong computer science fundamentals, including data structures, system design, and scalable architecture principles.
Ability to operate as a product-minded engineer, defining problems, shaping solutions, and driving execution with minimal direction.
Experience working with AI tools and automation as part of daily engineering workflows.
Strong understanding of AI limitations, including hallucinations, safety constraints, and evaluation challenges.
Security-conscious mindset with experience handling sensitive or regulated user data.

Own the end-to-end development of AI-powered product features, from opportunity identification and prototyping through production launch and iteration.
Design and build cross-cutting AI services such as retrieval systems, context orchestration, evaluation pipelines, and safety guardrails that support multiple product areas.
Act as a hybrid product and engineering owner by defining success metrics, validating hypotheses with customers, and shaping user experiences through working software.
Develop backend systems, APIs, and data models that enable scalable AI functionality across core platform workflows.
Build and maintain shared AI infrastructure including prompt frameworks, tooling standards, observability systems, and reusable components.
Design system architectures that enable safe, structured use of longitudinal data across product, billing, support, and operational systems.
Implement evaluation, monitoring, and feedback loops to continuously improve model performance and user outcomes in production.
Lead AI-augmented engineering practices, leveraging modern AI tools to accelerate development and mentoring other engineers in their use.
Collaborate across product, design, and data teams to ensure alignment between technical execution and user impact.
Ensure all AI systems are designed with safety, security, and reliability considerations, including handling sensitive education data responsibly.

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