About the role
Role Overview
We are seeking an experienced Electrical Engineering or Computer Science expert to lead a team of technical trainers developing realistic, terminal-based scientific and engineering tasks for a frontier AI training and evaluation initiative.
In this role, you will oversee a pod of approximately 5–10 trainers, review technical tasks for accuracy, reproducibility, and evaluation quality, and ensure tasks reflect authentic computer science, software systems, and electrical engineering workflows. You will also mentor trainers, maintain quality standards, and drive consistent delivery of challenging technical tasks.
What you'll do:
Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of scientific and engineering tasks.
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, ML 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 evaluate meaningful technical outputs such as functional correctness, performance, frequency response, waveforms, timing, efficiency, and system behavior.
Identify technical inaccuracies, flawed assumptions, incorrect tolerances, and opportunities to bypass computation, simulations, or automated evaluations.
Provide clear, actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
Onboard trainers, allocate work, monitor quality and throughput, escalate blockers, and maintain technical documentation and best practices.
What we're looking for:
Ph.D., postdoctoral experience, or equivalent advanced technical experience 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), with advanced Linux and terminal proficiency.
Hands-on expertise in at least one major area: 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 reliability of technical results.
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 providing precise technical feedback and documenting quality standards.
Nice to have:
Experience with HPC and systems development tools such as MPI, OpenMP, CUDA, LLVM/GCC, CMake, gdb, Valgrind, perf, sanitizers, or Slurm.
Familiarity with electrical engineering and hardware simulation tools such as ngspice, Xyce, Icarus Verilog, Verilator, GHDL, Yosys, pandapower, PyPSA, MATPOWER, openEMS, GNU Radio, or QEMU.
Familiarity with Docker, Git, CI/CD pipelines, and automated testing frameworks such as pytest.
Experience developing technical benchmarks, programming challenges, simulation-based evaluations, or automated grading systems.
Experience developing or evaluating AI coding agents, terminal-based agents, or LLM-generated technical solutions.
Open-source development or maintainer experience, particularly reviewing and validating external contributions.
Industry experience in semiconductors, high-performance computing, power utilities, telecommunications, or embedded systems.
Prior experience in AI training, technical quality control, benchmark development, or evaluation programs.
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