Member of Technical Staff, Platform

Abaka AI•Mountain View, CA

About The Position

About Abaka Abaka AI 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, Abaka AI provides the foundation for building high-performance AI systems. About the Role As a Member of Technical Staff, Platform, you'll build full-stack product features Expers Talent platform for our 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 in building AI Agents into productinos
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.