Remote AI Data Trainer: Hardware & Systems

Alignerr Corp.; United States; Remote; MXN 907,000 - 1,555,000; Part time

Benefits offered by this job

Competitive pay

Flexible remote work

Cutting-edge AI projects

Exposure to LLMs

Freelance autonomy

Job summary

Alignerr is seeking a Computer Engineering - AI Data Trainer to design advanced hardware problems and author rigorous ground-truth solutions for AI training. The role involves auditing AI-generated code and OS kernels for accuracy and efficiency, and providing structured feedback to improve model reasoning.

The ideal candidate holds an advanced degree in computer engineering or related hardware-focused CS field and can clearly communicate complex hardware concepts in writing.

Qualifications

• Masters or PhD in Computer Engineering, CS with hardware focus, or related field.
• Deep knowledge in Computer Architecture, Embedded Systems, Digital Logic, or OS.
• Ability to clearly document highly technical hardware concepts and low-level software logic.
• High precision in bit-level operations, clock-cycle timing, and documentation.
• No AI experience required.

Responsibilities

• Develop complex problems across RISC‑V / ARM, FPGA, memory management, and HW-SW co-design.
• Author ground-truth solutions with assembly, HDL snippets, and diagrams as golden responses.
• Auditing AI-generated code (C/C++, Verilog, VHDL) and OS kernels for accuracy.
• Refine reasoning by identifying race conditions, memory leaks, timing issues and provide feedback.

Skills

Computer Architecture

Embedded Systems

Digital Logic Design

Operating Systems

Analytical Writing

Attention to Detail

Education

Master's or PhD in Computer Engineering / Computer Science with hardware focus

Tools

Verilog

VHDL

Job description

Alignerr is seeking a Computer Engineering - AI Data Trainer to design advanced hardware problems and author rigorous ground-truth solutions for AI training. The role involves auditing AI-generated code and OS kernels for accuracy and efficiency, and providing structured feedback to improve model reasoning.

The ideal candidate holds an advanced degree in computer engineering or related hardware-focused CS field and can clearly communicate complex hardware concepts in writing.