Avionics Engineer - AI Model Training – Remote

Job Type: Contractor

Location: Remote

Role Description

If you’re a senior Avionics Engineer who thrives on precision, systems thinking, and safety-critical technical judgment, this is a unique opportunity to contribute directly to how the next generation of AI systems reason about aircraft electronics, flight systems, and aerospace engineering workflows. You’ll challenge and evaluate advanced language models on avionics engineering topics such as flight management systems, autopilot architecture, data buses, and safety-critical system design to strengthen model reasoning.

Your Profile

• 6+ years of professional experience as an Avionics Engineer, Aerospace Systems Engineer, Electrical Engineer in aviation, or a closely related role involving aircraft electronic systems.
• Significant hands-on work with avionics system design, integration, testing, troubleshooting, verification, validation, or certification support.
• Deep knowledge of avionics architectures, aircraft communication and navigation systems, flight deck systems, flight control interfaces, sensor integration, and safety-critical electronics.
• Proven experience interpreting technical requirements, reviewing engineering artifacts, identifying design gaps, and making disciplined tradeoffs in safety-critical environments.
• Bachelor’s degree in Aerospace Engineering, Electrical Engineering, Computer Engineering, Systems Engineering, or a related technical field; advanced degree or relevant certifications are a plus.

Key Responsibilities

• Evaluate AI-generated explanations, analyses, and technical recommendations related to avionics systems, aircraft electronics, flight controls, navigation, communications, surveillance, and cockpit systems.
• Challenge advanced language models with realistic avionics engineering prompts involving system architecture, fault diagnosis, requirements interpretation, certification constraints, and safety-critical decision-making.
• Review and refine AI-generated prompts, responses, rubrics, and model outputs for technical accuracy, completeness, clarity, and domain relevance.
• Identify subtle technical errors, unsafe assumptions, missing constraints, ambiguous reasoning, or oversimplified explanations in AI-generated avionics content.
• Provide structured feedback that helps AI systems improve reasoning around aircraft system integration, redundancy, reliability, maintainability, verification, and compliance.
• Shape AI communication standards for how complex avionics concepts should be explained to engineers, operators, technical reviewers, and non-specialist audiences.
• Help define what strong, safe, and technically rigorous AI performance looks like in aerospace and avionics contexts.