$35 - $60/hr
Full-time
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Job description
Computer Engineering – AI Data TrainerLocation: RemoteAbout The JobAt 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.OrganizationAlignerrPosition: Computer Engineering – AI Data TrainerType: Hourly ContractCompensation: $35–$60 /hourLocation: RemoteCommitment: 10–40 hours/weekWhat You'll DoDevelop 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.RequirementsAdvanced 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.PreferredPrior 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 UsCompetitive 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.J-18808-Ljbffr
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Frequently asked questions
Q: What skills or qualities help someone succeed as a Computer Engineer?
A: To succeed as a Computer Engineer, key technical skills include proficiency in programming languages such as C++, Java, and Python, as well as expertise in computer architecture, algorithms, and data structures. Additionally, soft skills like strong problem-solving abilities, effective communication, and teamwork are crucial for collaborating with cross-functional teams and presenting complex technical ideas to stakeholders. By combining these technical and soft skills, Computer Engineers can design, develop, and implement innovative solutions, drive project success, and advance in their careers through leadership and technical expertise.
Q: What is the career path for a Computer Engineer?
A: A Computer Engineer's typical career progression involves starting as an entry-level Hardware or Software Engineer, progressing to mid-level roles such as Senior Engineer or Technical Lead, and eventually becoming a Senior Technical Lead or Engineering Manager. Along the way, they can develop skills in areas like embedded systems, artificial intelligence, cybersecurity, and project management, with opportunities to specialize in specific domains like robotics, networking, or data analytics. Long-term, Computer Engineers may pursue leadership roles, start their own companies, or transition into related fields like data science, product management, or research and development.