AI Data Trainer - Computer Engineering (Remote, Contract)

Alignerr Corp.; Sheffield (TX); Remote; USD 48,000 - 83,000; Part time

Benefits offered by this job

Competitive pay

Flexible remote work

Job summary

Alignerr is seeking a Computer Engineering - AI Data Trainer for remote, hourly work. You will design advanced hardware/software challenges, author golden solutions including HDL snippets, and assess AI-generated code for correctness and efficiency.

Youll help improve model reasoning by identifying edge cases and documenting failure modes, enabling robust AI training. A Masters or PhD in a hardware-focused field is preferred, with strong knowledge in computer architecture and embedded systems.

Qualifications

• Masters (pursuing or completed) or PhD in Computer Engineering, Computer Science with hardware focus, or related field.
• Strong foundational knowledge in Computer Architecture, Embedded Systems, Digital Logic Design, or Operating Systems.
• Analytical writing: ability to communicate highly technical hardware concepts clearly in written form.
• Attention to detail: precision in bit-level operations, clock-cycle timing, and technical documentation.
• No AI experience required.

Responsibilities

• Develop complex problems across domains like RISC-V/ARM architecture, FPGA development, memory management, and HW-SW co-design.
• Author ground-truth solutions with step-by-step technical content including HDL snippets and diagrams.
• Technical auditing of AI-generated code (C/C++, Verilog, VHDL), gate designs, and OS kernels for accuracy and efficiency.
• Refine AI reasoning by identifying logical fallacies and timing issues, providing structured feedback.

Education

Masters or PhD in Computer Engineering/CS with hardware focus

Job description

Alignerr is seeking a Computer Engineering - AI Data Trainer for remote, hourly work. You will design advanced hardware/software challenges, author golden solutions including HDL snippets, and assess AI-generated code for correctness and efficiency.

Youll help improve model reasoning by identifying edge cases and documenting failure modes, enabling robust AI training. A Masters or PhD in a hardware-focused field is preferred, with strong knowledge in computer architecture and embedded systems.