Electrical Engineering & Computer Science Task Lead (AI Projects)

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.