Fullstack Product Engineer

Turing•New York, NY

6d•$180,000 - $220,000•Onsite

About The Position

We’re looking for a Senior Product Engineer—a single, broad role that combines domain understanding, technical fluency, and architectural judgment, leveraging AI tools to deliver end-to-end across the stack. Product Engineers own features from concept through production: shaping requirements with business partners, designing the architecture, building the UI and backend, and integrating data and services. This isn’t a traditional “full-stack developer” role. It’s a role for engineers who want to own outcomes, not just tickets—who can sit with a business user, understand the problem, make sound architectural decisions, and deliver a working solution that’s production-ready and maintainable.

Requirements

8+ years of software engineering experience with demonstrated ability to deliver features end-to-end across backend and frontend.
Strong proficiency in Python and Java, with sound architectural instincts (when to build vs. reuse, how to decompose services, when to simplify).
Fluency with modern AI development tools (Claude Code, MCP servers, LLM APIs) and an ability to use them productively while maintaining quality and correctness.
Experience working directly with business stakeholders—not just taking tickets, but shaping requirements and validating solutions.
The business context to make the right tradeoffs: you understand that technical decisions have business consequences and vice versa.
Strong communication skills: you can explain technical decisions to non-technical partners and advocate for engineering quality without creating friction.
A builder’s mindset: you prefer shipping working software over perfecting designs, while maintaining the quality bar for production systems.

Nice To Haves

Familiarity with Vue.js or a modern frontend framework (React acceptable if you can ramp quickly on Vue).
Experience with event-driven architecture, workflow orchestration, or data pipeline design.
A track record of building internal tools or platforms that make other teams more productive.

Responsibilities

Own features end-to-end: from discovery and requirements shaping through architecture, implementation, testing, and production deployment.
Partner directly with business stakeholders to understand workflows, pain points, and desired outcomes—then translate that into well-scoped, deliverable solutions.
Make sound architectural decisions: choose the right patterns, data models, and integration approaches based on business context and technical tradeoffs.
Build across the stack: backend services (Java/Spring), frontend components (Vue.js), API contracts, and data integrations.
Leverage modern AI development tools (Claude Code, MCP, LLM APIs) to accelerate delivery—prototyping, code generation, analysis, and testing—while maintaining strong engineering judgment and validation.
Contribute to platform quality: code reviews, testing discipline, observability, and documentation where it adds leverage.
Mentor mid-level engineers through design conversations, pairing, and code review—raising the team’s ability to deliver independently.
Navigate ambiguity: make pragmatic decisions with incomplete information and adjust course as understanding deepens.