Electrical Engineering & Computer Science Task Lead (AI Projects)
Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on Oct 15, 2026.
About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About the Role
We are seeking an experienced Computer Science, Electrical Engineering, or Computer Engineering Expert to lead a team of technical trainers developing realistic, terminal-based tasks for advanced AI training and evaluation. You will oversee a pod of approximately 5-10 trainers, review tasks for technical accuracy, reproducibility, and grading reliability, and ensure they reflect authentic software, computational, and electrical engineering workflows. This role combines advanced technical expertise, computational problem-solving, quality assurance, and team leadership to help evaluate and improve next-generation AI systems.
CAPACITY: Minimum 40 hours per week, at least 4 hours per day
SENIORITY: Senior / Technical Lead
LANGUAGE: Strong written English
Key Responsibilities
Lead and mentor a team of 5-10 technical trainers, ensuring consistent quality, productivity, and timely task delivery.
Review technical tasks end to end, including problem statements, codebases, circuit netlists, hardware description languages (HDL), datasets, computational environments, reference solutions, and automated tests.
Validate tasks involving 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 reflect realistic software engineering, research, and electrical engineering workflows requiring meaningful, multi-step technical reasoning.
Verify technical correctness across 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 ensure reproducible results.
Identify unreliable tests caused by nondeterministic behavior, race conditions, hardware-dependent performance thresholds, or simulation convergence issues.
Review automated graders to ensure they assess meaningful technical outputs, including functional correctness, performance, frequency response, waveforms, timing, efficiency, and system behavior.
Identify technical inaccuracies, flawed assumptions, incorrect tolerances, and ways tasks could be bypassed without genuine computation or engineering analysis.
Provide actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
Onboard trainers, allocate work, monitor quality and throughput, resolve technical blockers, and maintain technical documentation and best practices.
Requirements
- PhD, postdoctoral experience, or equivalent advanced technical expertise in Computer Science, Electrical Engineering, Computer Engineering, or a closely related discipline.
Strong programming skills in C/C++, Rust, Python, Julia, Fortran, MATLAB/Octave, or hardware description languages (HDL).
Advanced proficiency in Linux and command-line environments.
Hands-on expertise in at least one major area, such as parallel computing, systems programming, scientific software development, circuit simulation, signal processing, power systems, or digital hardware design.
Strong 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 technical reliability.
Strong analytical judgment and 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 precise technical feedback and documentation.