Embedded Systems and Firmware Engineer, AI Expert (Remote, US)

Weekday AI

8h • $100 - $150 • Remote

About The Position

This remote, part-time independent contractor role is for an AI-focused subject-matter expert in embedded systems and firmware engineering. The engineer will work with research and engineering teams at a leading AI lab to improve the accuracy, reliability, and technical depth of AI-generated engineering solutions. Work includes developing challenging technical tasks, creating reference solutions, evaluating AI model outputs, and establishing benchmarks for complex embedded systems and hardware engineering problems. The role requires at least 20 hours per week on weekdays, with a flexible remote schedule. Engagements may be extended, shortened, or concluded early based on project requirements and performance. Payment is weekly through Stripe or Wise, based on services rendered. Applicants must be based in the United States; H-1B and STEM OPT candidates cannot be supported. The role does not require access to confidential or proprietary information belonging to a current or former employer, client, or institution.

Requirements

• At least 4 years of professional experience in embedded systems, firmware development, operating system kernel engineering, or chip design.
• Experience working at a recognized organization in industries such as semiconductors, operating systems, consumer devices, automotive, aerospace, or hardware technology.
• Strong proficiency in production-level C and/or C++ programming, particularly for resource-constrained or performance-critical systems.
• Hands-on experience in at least one of the following: Linux kernel and device drivers; RTOS and firmware; or RTL design and verification.
• For Linux kernel and device drivers: experience with kernel development, driver architecture, system calls, memory management, and low-level debugging.
• For RTOS and firmware: experience with real-time operating systems, embedded firmware development, hardware-software integration, and device-level programming.
• For RTL design and verification: experience with Verilog, SystemVerilog, VHDL, UVM, digital logic design, and hardware verification.
• Strong understanding of operating system internals, computer architecture, memory management, concurrency, and low-level system behavior.
• Demonstrable career progression and ownership of complex engineering responsibilities.
• Excellent written communication skills and the ability to explain complex technical concepts and engineering decisions clearly.
• Ability to commit reliably to at least 20 hours per week during weekdays.
• Must be based in the United States.
• H-1B and STEM OPT candidates cannot be supported.

Nice To Haves

• Experience working on production-grade embedded systems or performance-critical software.
• Familiarity with hardware-software co-design, chip architecture, or semiconductor development workflows.
• Experience debugging complex system-level issues across hardware and software layers.
• Exposure to AI-powered development tools or large language models (LLMs).
• Experience evaluating AI-generated code, developing technical benchmarks, or creating structured engineering assessment rubrics.

Responsibilities

• Collaborate with AI research and engineering teams to identify technical knowledge gaps and improve model performance across embedded systems, firmware, kernel engineering, device drivers, and hardware design.
• Design domain-specific engineering tasks and develop accurate, well-structured reference solutions covering low-level programming and systems engineering concepts.
• Evaluate AI-generated solutions for technical correctness, efficiency, reliability, and practical feasibility, and provide clear, structured written feedback.
• Develop and assess low-level engineering tasks involving memory management, concurrency, synchronization, device drivers, RTOS behavior, and hardware description languages.
• Review solutions related to Linux kernel internals, firmware architecture, embedded software, and hardware design and verification.
• Create detailed evaluation rubrics to assess correctness, safety, performance, and reliability of systems-level software and hardware design solutions.
• Identify logical errors, implementation issues, performance bottlenecks, and potential safety concerns in AI-generated technical responses.
• Collaborate with other subject-matter experts to maintain consistency, accuracy, and quality across AI training datasets.

What This Job Offers

• Job Type: Part-time
• Career Level: Senior
• Education Level: No Education Listed