Remote QA Engineer (AI / ML Focus)

bout Special Projects Engineering LLC

Special Projects Engineering is a small, elite team based in Seattle tackling complex, high-impact problems using advanced AI and automation. We work across domains like autonomous systems, predictive infrastructure, synthetic biology, and next-gen defense tech. When something hasn’t been done before, we don’t shy away — we build it.

What You’ll Do

As a QA Engineer focused on ML / AI systems , your mission is to ensure the reliability, accuracy, and stability of intelligent systems that operate in messy, real-world environments. You’ll design robust testing frameworks and evaluation pipelines to validate everything from perception models to full-stack ML deployments.

Responsibilities :

Develop and maintain automated test suites for machine learning pipelines and AI-enabled systems

Create testing strategies for models handling visual, audio, sensor, and textual data

Validate data inputs, model outputs, performance benchmarks, and edge-case behavior

Build tools for continuous integration and regression testing across ML components

Work with ML engineers to define evaluation metrics and acceptable thresholds

Help ensure models are safe, stable, and robust before deployment

Identify gaps in test coverage and build test plans that scale with the codebase

What We’re Looking For

3+ years in software QA, test automation, or related role

Hands-on experience testing ML models or AI systems in production environments

Strong Python skills; bonus if you’ve worked with PyTorch, TensorFlow, or JAX

Familiarity with ML lifecycle and model validation techniques

Experience with CI / CD pipelines, test frameworks, and debugging tools

Detail-oriented mindset with strong instincts for edge cases and failure modes

Bonus : background in data validation, simulation environments, or robotics

Why Join Us

Remote first company

Competitive salary and equity

Flexible hours and remote-friendly culture

Full health, dental, and vision insurance

High-end GPU hardware and compute resources

Work on real-world AI systems with a high-caliber team

Fast-moving environment where your work directly impacts production

If you care about building AI that actually works, works well, and works safely — we’d love to talk.