Remote AI Data Trainer: Computer Engineering

Alignerr Corp.; Ireland; Remote; EUR 43,000 - 74,000; Part time

Benefits offered by this job

Autonomy

Flexible remote work

Global collaboration

Job summary

Alignerr is seeking a highly skilled Computer Engineering - AI Data Trainer to work remotely on developing advanced engineering challenges and gold-standard solutions for AI training. You will document and verify complex hardware-software interactions, including HDL snippets and assembly code, to improve model accuracy.

You will audit AI-generated code and OS-level concepts, identify timing and concurrency issues, and provide structured feedback to enhance model reasoning and reliability.

Qualifications

• Advanced degree in Computer Engineering, Computer Science with hardware focus, or closely related field.
• Strong knowledge in core areas like Computer Architecture, Embedded Systems, Digital Logic Design, or Operating Systems.
• Ability to communicate highly technical hardware concepts clearly in written form.
• High precision in checking bit-level operations, clock-cycle timing, and technical docs.

Responsibilities

• Develop complex problems across domains like RISC-V/ARM, FPGA, memory management, and HW-SW co-design.
• Author rigorous gold-standard solutions with assembly, HDL snippets, and architectural diagrams for AI training.
• Perform technical auditing of AI-generated code (C/C++, Verilog, VHDL), logic designs, and OS kernels for accuracy and standards.
• Refine AI reasoning by identifying race conditions, memory leaks, and timing issues, providing structured feedback.

Skills

Domain Expertise

Analytical Writing

Attention to Detail

No AI Experience Required

Education

Master’s or PhD in Computer Engineering/CS (hardware focus)

Job description

Alignerr is seeking a highly skilled Computer Engineering - AI Data Trainer to work remotely on developing advanced engineering challenges and gold-standard solutions for AI training. You will document and verify complex hardware-software interactions, including HDL snippets and assembly code, to improve model accuracy.

You will audit AI-generated code and OS-level concepts, identify timing and concurrency issues, and provide structured feedback to enhance model reasoning and reliability.