Computer Engineering

Alignerr partners with leading AI research teams to build and train cutting-edge AI models. The Computer Engineering - AI Data Trainer will develop complex engineering challenges, author technical solutions, and evaluate AI-generated code to improve model reasoning.

Responsibilities:

• Develop Complex Problems: Design advanced computer engineering challenges across domains like RISC-V/ARM architecture, FPGA development, memory management, and hardware-software co-design
• Author Ground-Truth Solutions: Create rigorous, step-by-step technical solutions, including assembly code, hardware description language (HDL) snippets, and architectural diagrams that serve as 'golden responses' for AI training
• Technical Auditing: Evaluate AI-generated code (C/C++, Verilog, VHDL), logic gate designs, and operating system kernels for technical accuracy, efficiency, and adherence to industry standards
• Refine Reasoning: Identify logical fallacies in AI reasoning—such as race conditions, memory leaks, or improper timing constraints—and provide structured feedback to improve the model's 'thinking' process

Requirements:

• Advanced Degree: Masters (pursuing or completed) or PhD in Computer Engineering, Computer Science with a hardware focus, or a closely related field
• Domain Expertise: Strong foundational knowledge in core areas such as Computer Architecture, Embedded Systems, Digital Logic Design, or Operating Systems
• Analytical Writing: The ability to communicate highly technical hardware concepts and low-level software logic clearly and concisely in written form
• Attention to Detail: High level of precision when checking bit-level operations, clock-cycle timing, and technical documentation
• No AI experience required
• Prior experience with data annotation, data quality, or evaluation systems
• Proficiency in engineering software concepts (e.g., SolidWorks, MATLAB, ANSYS) to evaluate AI-generated code or workflows