Software Engineer I, Quality

Handshake•San Francisco, CA

1d•$136,000 - $170,000•Onsite

About The Position

We’re hiring a Software Engineer to join our Quality team. The team builds the systems that define, select, and enforce quality standards across the data and evaluations we deliver to leading AI labs. You’ll work on infrastructure that evaluates complex outputs submitted by fellows, such as coding tasks designed to challenge advanced AI models. These quality checks run throughout the production process: providing real-time feedback to fellows, supporting internal operators, and validating outputs before they reach customers.

Requirements

2+ years of experience building and maintaining backend applications, services, or data-intensive systems
Strong software engineering fundamentals, including system design, testing, debugging, and operational reliability
A data-oriented approach to solving problems and measuring whether a system is working
Experience working with APIs, databases, data pipelines, or distributed systems
The ability to navigate ambiguous problems, break them into manageable pieces, and deliver iteratively
Strong communication and collaboration skills across technical and non-technical teams
A quality-focused mindset and an interest in building systems that make difficult-to-measure outcomes more rigorous

Nice To Haves

Experience with machine learning, applied AI, or AI-enabled products
Familiarity with model evaluation, experimentation, recommendation systems, or ranking systems
Experience with annotation, data-labeling, tasking, or workflow-orchestration platforms
Experience building developer platforms, internal tools, or extensible APIs
Familiarity with MCP, agentic systems, or AI-native development tools
Experience working in a fast-moving startup or similarly ambiguous environment

Responsibilities

Build and scale backend systems that evaluate and improve the quality of data delivered to frontier AI labs
Develop infrastructure for running quality checks in real time and before final customer delivery
Help design recommendation systems that select the most effective quality checks for each project
Build data pipelines and measurement systems that make complex, subjective quality signals more observable and actionable
Develop APIs and platform capabilities that allow other teams and tools to use the Quality platform
Help surface Quality capabilities through MCP and other AI-native interfaces used by internal operators
Partner with engineers, researchers, product teams, and operations stakeholders to turn ambiguous quality problems into scalable technical solutions
Improve the reliability, efficiency, and cost-effectiveness of quality workflows as the business grows