Electrical Engineering & Computer Science Task Lead (AI Projects)

Gramian Consultancy is a boutique firm specializing in IT professional services and engineering talent solutions. Leveraging deep expertise in software engineering and leadership, Gramian partners with companies to build high-performing teams by connecting them with professionals who align closely with their technical needs. We are currently looking for an experienced expert in Computer Science, Electrical Engineering, or Computer Engineering to lead a team of technical trainers. This role involves developing realistic, terminal-based tasks for advanced AI training and evaluation. You will manage a pod of 5 to 10 trainers, ensuring the technical accuracy, reproducibility, and grading reliability of tasks that simulate authentic workflows in software, computational, and electrical engineering. This position demands a combination of advanced technical knowledge, computational problem-solving skills, quality assurance, and leadership to help shape next-generation AI systems. The role requires a minimum commitment of 40 hours per week, with at least 4 hours daily, and is positioned at a senior or technical lead level. Strong written English communication is essential.

Key Responsibilities

- Lead and mentor a team of 5 to 10 technical trainers, maintaining high standards of quality, productivity, and timely delivery of tasks.
- Conduct thorough reviews of technical tasks, including problem statements, codebases, circuit netlists, hardware description languages (HDL), datasets, computational environments, reference solutions, and automated tests.
- Validate tasks spanning high-performance computing (HPC), parallel computing, algorithms, distributed systems, compilers, operating systems, databases, machine learning systems, GPU computing, circuit design, signal processing, power systems, and embedded systems.
- Ensure tasks accurately reflect realistic workflows in software engineering, research, and electrical engineering, requiring meaningful multi-step technical reasoning.
- Verify technical correctness across various dimensions such as algorithm complexity, numerical accuracy, memory management, concurrency, circuit laws, power balance, signal processing, timing constraints, and system stability.
- Validate compilers, simulation tools, software dependencies, computational environments, and build processes to guarantee reproducible results.
- Identify and address unreliable tests caused by nondeterministic behavior, race conditions, hardware-dependent performance thresholds, or simulation convergence issues.
- Review automated graders to confirm they assess meaningful technical outputs, including functional correctness, performance, frequency response, waveforms, timing, efficiency, and overall system behavior.
- Detect technical inaccuracies, flawed assumptions, incorrect tolerances, and potential task bypasses that circumvent genuine computation or engineering analysis.
- Provide actionable technical feedback, track revisions, and ensure quality issues are resolved prior to task approval.
- Onboard new trainers, allocate work effectively, monitor quality and throughput, resolve technical blockers, and maintain comprehensive technical documentation and best practices.

Required Qualifications

- PhD, postdoctoral experience, or equivalent advanced technical expertise in Computer Science, Electrical Engineering, Computer Engineering, or a closely related discipline.
- Strong programming skills in languages such as C/C++, Rust, Python, Julia, Fortran, MATLAB/Octave, or hardware description languages (HDL).
- Advanced proficiency with Linux and command-line environments.
- Hands-on expertise in at least one major technical domain, including parallel computing, systems programming, scientific software development, circuit simulation, signal processing, power systems, or digital hardware design.
- Solid understanding of algorithms, computational systems, numerical methods, software testing, or electrical engineering principles.
- Experience reviewing complex codebases, technical implementations, engineering simulations, or research outputs.
- Ability to evaluate algorithm correctness, computational performance, numerical accuracy, concurrency behavior, and overall technical reliability.
- Strong analytical judgment with the ability to identify technical errors, edge cases, race conditions, and flawed engineering assumptions.
- Experience mentoring, reviewing, or leading small technical teams.
- Excellent written communication skills for delivering precise technical feedback and maintaining documentation.

This role offers the opportunity to lead a specialized team in developing cutting-edge AI evaluation tasks, contributing directly to the advancement of AI systems through rigorous technical oversight and leadership.