Role Overview
As a founding AI Evaluation Engineer, you will design and build the evaluation, testing, and observability infrastructure that ensures production-grade AI agents perform reliably, safely, and efficiently. You will develop automated testing systems for complex reasoning, multi-turn conversations, and agent interactions involving APIs, databases, and code execution. Working across AI engineering, software testing, and infrastructure, you will create scalable evaluation pipelines that go beyond traditional QA and manual prompt testing. You will establish robust regression testing, statistical evaluation methods, and adversarial scenarios to identify weaknesses before deployment.
Accountabilities
- Design, build, and maintain automated CI/CD testing pipelines to validate AI agent behavior before deployment and throughout the development lifecycle.
- Develop and maintain golden datasets containing regression prompts, edge cases, and synthetic scenarios to measure agent accuracy, safety, performance, and latency.
- Implement fast, cost-effective, deterministic evaluation checks before introducing LLM-as-a-judge approaches, including JSON schema validation, regular expressions, API status checks, and embedding similarity.
- Create comprehensive test suites for multi-turn agent conversations, evaluating context retention, state consistency, reasoning behavior, and safeguards against infinite tool-calling loops.
- Design and implement adversarial testing scenarios to assess resilience against prompt injection, malformed inputs, unexpected tool responses, and other potential security vulnerabilities.
- Build statistical evaluation frameworks to measure non-deterministic AI behavior using pass rates across multiple runs, confidence intervals, and other quantitative reliability metrics.
- Integrate evaluation workflows into GitHub Actions or comparable CI systems using SDK-level execution and automated testing practices.
- Implement observability and tracing solutions using tools such as OpenTelemetry, LangSmith, Arize Phoenix, W&B Weave, or equivalent platforms to monitor agent execution and diagnose failures.
- Optimize evaluation efficiency and cost through caching, mocking, and other techniques that reduce unnecessary LLM API calls.
- Validate structured outputs, including JSON schemas, OpenAPI specifications, and function-calling payloads, to ensure agents interact reliably with external systems.
- Evaluate and improve agent performance under load, accounting for variable response times and non-deterministic endpoint behavior.
- Independently design and own the evaluation architecture, establishing best practices, testing standards, and scalable processes for ongoing quality assurance.
What we offer
- Fully remote work: Work from anywhere with a reliable internet connection, with the flexibility to choose your preferred time zone and working hours.
- Flexible schedule: Organize your working day around your productivity while maintaining the required overlap with US Central Time.
- Professional development: Access paid training and other opportunities to expand your technical expertise and support your career growth.
- Modern technology: Work with contemporary AI tools, frameworks, and engineering practices in an environment that values innovation and continuous improvement.
- Consistent project opportunities: Benefit from a steady flow of engaging and challenging product development work.
- Performance recognition: Strong performance and dedication may be rewarded through annual pay increases.
- Negotiable compensation: Your starting hourly rate will be determined based on your skills, experience, and qualifications.
- Independent contractor arrangement: Compensation is based on hours worked, with payments made monthly via wire transfer.
- Flexible working environment: Enjoy the autonomy to work in a setting that supports your focus, independence, and professional development.
Requirements
- Strong proficiency in Python or TypeScript, particularly for API testing, headless execution, asynchronous programming, and automated testing systems.
- Hands-on experience building LLM evaluation pipelines, AI testing frameworks, or statistical testing systems capable of measuring performance across repeated runs.
- Experience integrating automated tests into CI/CD environments, particularly GitHub Actions or similar platforms, with SDK-level execution.
- Strong expertise in validating structured data and outputs, including JSON schema, OpenAPI specifications, and function-calling payloads.
- Familiarity with observability and tracing tools such as OpenTelemetry, LangSmith, Arize Phoenix, W&B Weave, or custom logging solutions.
- Ability to independently design, implement, and maintain an evaluation architecture from scratch, with a strong sense of ownership and technical judgment.
- Excellent written and spoken English, with the ability to communicate technical findings clearly in a distributed team.
- Availability to overlap with US Central Time business hours for at least three hours each day.
- Experience with code execution sandboxes or AI agent frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI.
- Strong understanding of embedding models, vector databases, and their applications in AI evaluation and similarity-based testing.
- Experience with load testing non-deterministic endpoints and assessing system behavior under varying workloads.
- A proactive, analytical approach to problem-solving, with the ability to work independently, adapt to evolving technologies, and continuously improve testing methodologies.
