Amadeus Search
Position Details
Role:
Data Tooling Engineer
Location:
San Francisco, CA (on-site, 5 days/week)
Compensation:
$135k – $170k + 0.1%–0.17% equity
Stage:
Seed/early-stage, well-funded ($17.5M+)
Industry:
AI / Developer Tools
Team size:
~11 employees
Founded:
2024
About the Company An early-stage startup is tackling one of the biggest bottlenecks in AI development:
the lack of high-quality post-training data for coding LLMs . They are building a
gamified coding platform
that attracts skilled engineers to generate abundant, high-quality training data.
The mission is to enable the
next generation of autonomous coding LLMs —pushing beyond productivity tools into systems that empower anyone to design and build production-ready software. The company is backed by top investors, well-known AI leaders, and notable founders.
About the Role As a
Data Tooling Engineer , you will focus on
developer experience
and own projects across the full technical lifecycle of data pipelines—from designing schemas and formats to building tooling, SDKs, and documentation for contributors. This is a
high-ownership role
with immediate impact on both engineering workflows and model quality.
Responsibilities
Own projects end-to-end: prototype, ship, maintain, and iterate based on developer feedback
Build tooling for new data formats, productionize workflows, and create contributor sandboxes/SDKs
Define schemas, metadata, and standards for data pipelines in collaboration with research partners
Champion developer experience with clear documentation and streamlined workflows
Establish and enforce data quality standards (automated checks, eval harnesses, reviewer workflows)
Monitor and debug systems to improve reliability, latency, and contributor success rates
Tech Stack React, Python, Node.js, Go, AWS, Docker, CI/CD pipelines
Candidate Profile
0–3 years of engineering experience, ideally in environments with
large or complex repositories
High-agency, pragmatic, and resourceful—comfortable leveraging
AI tools
in development
Experience with
open-source contributions
or strong developer tooling exposure a plus
Thrives in fast-moving, hackathon-like startup environments
Work Environment
On-site in San Francisco, 5 days/week
Intense, collaborative, “builder” culture—described as a
long hackathon with friends
Backed by tier-one investors and prominent AI researchers/founders
#J-18808-Ljbffr
Data Tooling Engineer
Location:
San Francisco, CA (on-site, 5 days/week)
Compensation:
$135k – $170k + 0.1%–0.17% equity
Stage:
Seed/early-stage, well-funded ($17.5M+)
Industry:
AI / Developer Tools
Team size:
~11 employees
Founded:
2024
About the Company An early-stage startup is tackling one of the biggest bottlenecks in AI development:
the lack of high-quality post-training data for coding LLMs . They are building a
gamified coding platform
that attracts skilled engineers to generate abundant, high-quality training data.
The mission is to enable the
next generation of autonomous coding LLMs —pushing beyond productivity tools into systems that empower anyone to design and build production-ready software. The company is backed by top investors, well-known AI leaders, and notable founders.
About the Role As a
Data Tooling Engineer , you will focus on
developer experience
and own projects across the full technical lifecycle of data pipelines—from designing schemas and formats to building tooling, SDKs, and documentation for contributors. This is a
high-ownership role
with immediate impact on both engineering workflows and model quality.
Responsibilities
Own projects end-to-end: prototype, ship, maintain, and iterate based on developer feedback
Build tooling for new data formats, productionize workflows, and create contributor sandboxes/SDKs
Define schemas, metadata, and standards for data pipelines in collaboration with research partners
Champion developer experience with clear documentation and streamlined workflows
Establish and enforce data quality standards (automated checks, eval harnesses, reviewer workflows)
Monitor and debug systems to improve reliability, latency, and contributor success rates
Tech Stack React, Python, Node.js, Go, AWS, Docker, CI/CD pipelines
Candidate Profile
0–3 years of engineering experience, ideally in environments with
large or complex repositories
High-agency, pragmatic, and resourceful—comfortable leveraging
AI tools
in development
Experience with
open-source contributions
or strong developer tooling exposure a plus
Thrives in fast-moving, hackathon-like startup environments
Work Environment
On-site in San Francisco, 5 days/week
Intense, collaborative, “builder” culture—described as a
long hackathon with friends
Backed by tier-one investors and prominent AI researchers/founders
#J-18808-Ljbffr