Member of Technical Staff, Platform

1 нед. назад

USALeadHybrid

frontendbackendfull-stackdata engineering

Build full-stack product features for the Expert Talent platform end-to-end, working across frontend and backend.

Обязанности

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.
- 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.

Требования

- 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 of building AI agents into products.
- 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.

Будет плюсом

- 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).

Условия

The base salary range for this position is $120,000 – $200,000 USD annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work at . This role is eligible for equity, as well as a comprehensive benefits package (health, dental, vision, PTO, flexible work schedule).

Другое

is built on one mission: to be the world's most trusted data partner for AI companies. More than 1,000 industry leaders across Generative AI, Embodied AI, and Automotive AI rely on us to power their data pipelines. With our headquarters in Silicon Valley—and teams in Paris, Singapore, and Tokyo—we support global partners with fast, reliable, and scalable data solutions.
Our offerings include a diverse catalog of off-the-shelf datasets (image, video, multimodal, reasoning, 3D, and beyond) as well as comprehensive data collection and annotation services. Whether teams need raw data, curated datasets, or full-cycle data engineering, provides the foundation for building high-performance AI systems.