Business Services & Consulting • all cities, WA 48
Computer Engineering Expert - AI Content Specialist (48)
all cities, WA 48On-sitePosted 1 day ago
Business Services & Consulting
About the Role
Computer Engineering Expert - AI Content Specialist
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 computer architecture and hardware design, embedded systems and IoT, networking and distributed systems, hardware security, and systems software and operating systems—documenting every failure mode so we can harden model reasoning.
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
Preferred:
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.
Why Join Us:
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.
Application Process (Takes 15-20 min)
Submit your resume
Complete a short screening
Project matching and onboarding
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Computer Engineering Expert - AI Content Specialist
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 computer architecture and hardware design, embedded systems and IoT, networking and distributed systems, hardware security, and systems software and operating systems—documenting every failure mode so we can harden model reasoning.
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
Preferred:
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.
Why Join Us:
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.
Application Process (Takes 15-20 min)
Submit your resume
Complete a short screening
Project matching and onboarding
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
What You'll Do
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.
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.
