Member of Technical Staff, Platform
$120,000–$200,000 year
On-siteMountain View Santa Clara County, California, United States
Job Summary
Design, build, and ship full-stack product features for the Expert Talent platform, spanning UI, API, and data layers with a bias toward rapid iteration. Own features end-to-end from product discussions through implementation, testing, and release, embedding AI directly into workflows via assistive UX and automation. Build evaluation pipelines and data models for AI-powered capabilities while leveraging modern AI coding tools to accelerate development. Partner closely with Product and Design to shape scope and UX, improving the performance, reliability, and observability of the assessment infrastructure. This generalist role supports Abaka AI's scaling platform for over 1,000 industry leaders in Generative, Embodied, and Automotive AI.
Required Qualifications
- 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
Desired Qualifications
- 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)
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