Principal AI Product Engineer
Skills
Computer ScienceAdoptionComplex SystemsContextDefaultsFoundationsReadiness
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 8+ years of engineering experience with end-to-end ownership of large business-critical systems
- Deep intuition for why models fail from experience predating the LLM boom
- Experience with classical NLP or ML to support sound architectural decisions
- Experience formulating architectural strategy at the organization level
- Track record of mentoring senior and staff engineers
- Proven ability to ship AI-powered products to production
- Experience with evaluation monitoring and safety for AI systems
- Ability to work independently with minimal guidance
- Strong judgment on build versus buy decisions
- Experience designing systems across multiple teams
Nice to have
- product-minded
- AI-native
- relentlessly driven
- decisive
- hands-on
- security-minded
- mission-aligned
Day to day
- Own outcomes end-to-end by turning high-leverage AI opportunities into shipped capabilities with clear measurement and iteration loops.
- Define AI technical direction by translating customer problems into practical strategy reference architectures and a roadmap that multiple teams can execute.
- Lead by implementation through rapid prototyping and reference implementations that prove feasibility de-risk decisions and raise team ambition.
- Help build shared foundations such as evaluation harnesses retrieval patterns safety guardrails observability and developer experience so teams can ship reliable AI repeatedly.
- Raise the bar across the organization by mentoring senior and staff engineers leading design reviews and aligning stakeholders to drive adoption.