Member of Technical Staff, Platform

Embedding VC•Mountain View, CA

6d•$120,000 - $200,000

About The Position

As a Member of Technical Staff, Platform, you'll build full-stack product features for our Expert Talent platform end-to-end—from a rough idea in a product discussion to a shipped, instrumented feature in front of real customers and users. You'll work across the stack (frontend, backend, and the glue between them) and work closely with cross-functional teams like product, project management, and leadership. This is a generalist product engineering role, and you need to have full-stack (FE and BE) fluency and a product mindset in the way you approach development. You care as much about whether a feature solves the customer and users' problems as you do about how it's built. Our Expert Talent platform is growing and scaling significantly in the 1-100 stage, so your impact will be immense and have a great deal of ownership from day one.

Requirements

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.
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.

Nice To Haves

Experience building or integrating LLM-powered features in production is a strong plus.
Knowledge of building AI agents into products.
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).

Responsibilities

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.