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- Design, build, and ship full-stack product features — UI, API, and data layer — with a strong bias toward shipping and iterating quickly.
- Own features end-to-end, from product discussion through implementation, testing, and release.
- Embed AI directly into product workflows: AI-powered features, assistive UX, and automation that make the product feel intelligent, not just functional.
- Build evaluation pipelines and data models for AI-powered features
- Leverage modern AI coding tools to accelerate development while maintaining a high engineering bar.
- Partner closely with Product and Design to shape scope and UX, not just implement a spec handed to you.
- Improve the performance, reliability, and observability of assessment infrastructure.
- 1+ years of professional software engineering experience building and shipping production features.
- Strong backend fundamentals: API design, data modeling, distributed systems, and relational databases.
- Comfortable working across the stack, including modern frontend frameworks (e.g. React/TypeScript) when the product needs it.
- Experience building or integrating LLM-powered features in production is a strong plus.
- Knowledge in building AI Agents into productinos
- Product-minded: you ask "why should we build this" and "is this the right solution" as naturally as "how do I build this," and you treat customer outcomes as the measure of success, not the technology you shipped.
- AI-native by default: you already use AI coding tools daily, and you've built or shipped features that use LLMs/AI
- Solid engineering fundamentals: testing, debugging, performance basics, and the judgment to know when to invest in each.
- Comfortable with ambiguity and evolving priorities
- High ownership, pragmatism, and a bias toward shipping.
- Startup or founder experience
- Experience building consumer-facing products at scale.
- ML and Deep Learning knowledge and experience
- Experience with microservices, event-driven architectures, and cloud platforms (AWS/GCP).
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