AI-Driven Backend Engineer — Data Pipelines + Equity (SF)

davidjoseph-co; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Equity

Job summary

Kinect in San Francisco, CA is seeking a Backend Engineer with a data-engineering lean to build data enrichment and ingestion pipelines and the platform infrastructure that doesn’t yet exist. You’ll work on AI-driven tooling and report to one of the two technical co-founders in a small engineering team.

The role emphasizes building from scratch, ownership, and the ability to translate customer needs into shipped features while adapting to a fast-paced startup environment and a highly iterative

Qualifications

• Experience using AI coding tools as part of daily development workflows.
• Strong understanding of end-to-end feature delivery and data-layer design.
• Demonstrated ownership of shipped features in a startup setting.
• Comfort with a fast-paced, high-commitment schedule (5–6 days per week).
• Exposure to e-commerce, D2C, or platform integrations is a plus.

Responsibilities

• Build and maintain data enrichment, normalization, and ingestion pipelines.
• Develop platform infrastructure and observability tooling from scratch.
• Work with AI coding tools as a core part of daily development, not just a helper.
• Translate customer feedback into shipped features, sometimes from meeting summaries.
• Contribute to systems and data-layer design decisions.

Skills

AI coding tools

Systems engineering

Startup ownership

Ambiguity handling

E-commerce exposure

Education

0–2 years experience

Tools

Fly.io

TypeScript

Job description

Kinect in San Francisco, CA is seeking a Backend Engineer with a data-engineering lean to build data enrichment and ingestion pipelines and the platform infrastructure that doesn’t yet exist. You’ll work on AI-driven tooling and report to one of the two technical co-founders in a small engineering team.

The role emphasizes building from scratch, ownership, and the ability to translate customer needs into shipped features while adapting to a fast-paced startup environment and a highly iterative