Electrical Engineering - AI Data Trainer

• Advanced Degree: Masters (pursuing or completed) or PhD in Electrical Engineering, Electronic Engineering, or a closely related field.
• Domain Expertise: Strong foundational knowledge in core areas such as VLSI, control systems, telecommunications, or power electronics.
• Analytical Writing: The ability to communicate highly technical concepts clearly and concisely in written form.
• Attention to Detail: High level of precision when checking mathematical equations, circuit logic, and technical documentation.
• No AI experience required

Preferred

• Prior experience with data annotation, data quality, or evaluation systems

Organization: Alignerr Position: Electrical Engineering - AI Data Trainer Type: Hourly Contract Compensation: $35-$60 /hour Location: Remote Commitment: 10-40 hours/week

What You'll Do

• Develop Complex Problems: Design advanced electrical engineering problems across domains like circuit analysis, power systems, signal processing, and electromagnetics to test AI performance.
• Author Ground-Truth Solutions: Create rigorous, step-by-step technical solutions and "golden responses" that the AI will use as a benchmark for learning.
• Technical Auditing: Evaluate AI-generated code, circuit diagrams, and mathematical proofs for technical accuracy, safety, and adherence to engineering standards (e.g., IEEE, NEC).
• Refine Reasoning: Identify logical fallacies in AI reasoning and provide structured feedback to help the model improve its "thinking" process for engineering tasks., * Competitive pay and flexible remote work.
• Collaborate with a team working on cutting-edge AI projects.
• Exposure to advanced LLMs and how they're trained.
• Freelance perks: autonomy, flexibility, and global collaboration.
• Potential for contract extension.

At Alignerr, we partner with the world's leading AI research teams and labs to build and train cutting-edge AI models. You'll challenge advanced language models on topics like embedded systems, signal processing, control systems and robotics, and engineering simulations-documenting every failure mode so we can harden model reasoning.