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Open nowPosted 14 hours ago

Technical Lead Electrical Engineering and Computer Science

Weekday AI

Where
India
Work mode
Remote
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Your applicationOpen nowTechnical Lead Electrical Engineering and Computer ScienceWeekday AI · India

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 14 hours ago

The posting

This role is for one of Weekday’s clients

Min Experience: 7+ years Location: Remote (India) JobType: full-time

We are seeking an experienced Technical Lead with a strong background in Electrical Engineering, Computer Science, or Computer Engineering to lead a team of technical trainers responsible for developing realistic, terminal-based scientific and engineering tasks for AI training and evaluation.

In this role, you will lead a team of approximately 5–10 technical trainers, oversee end-to-end task development and validation, and ensure that every task meets high standards of technical accuracy, reproducibility, and evaluation quality. You will work across software systems, scientific computing, and electrical engineering domains, ensuring that tasks reflect authentic engineering workflows and require meaningful, multi-step technical reasoning.

Requirements

Key Responsibilities

  • Team Leadership and Mentorship: Lead, mentor, and onboard a team of technical trainers. Allocate assignments, monitor productivity and quality, resolve blockers, and ensure timely delivery of high-quality technical tasks.
  • Technical Review and Validation: Review tasks end to end, including problem statements, codebases, circuit netlists, hardware description languages (HDLs), datasets, computational environments, reference solutions, and automated tests.
  • Scientific and Engineering Task Development: Validate tasks across 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.
  • Technical Accuracy: Verify algorithmic correctness, computational complexity, numerical precision, memory management, concurrency behavior, circuit laws, power balance, signal processing accuracy, timing constraints, and system stability.
  • Environment and Reproducibility: Validate compilers, simulation tools, software dependencies, build processes, and computational environments to ensure consistent and reproducible results.
  • Testing and Evaluation Quality: Review automated tests and graders to ensure they assess meaningful technical outcomes, including functional correctness, performance, frequency response, waveforms, timing, efficiency, and overall system behavior.
  • Reliability and Edge-Case Analysis: Identify nondeterministic tests, race conditions, hardware-dependent performance thresholds, simulation convergence issues, incorrect tolerances, flawed assumptions, and potential ways to bypass computational or simulation requirements.
  • Quality Assurance: Provide precise, actionable feedback, track revisions, and ensure identified issues are resolved before tasks receive final approval.
  • Technical Documentation: Establish and maintain technical guidelines, review standards, documentation, and best practices to ensure consistency across the team.
  • Continuous Improvement: Improve task development and evaluation processes, strengthen technical quality standards, and contribute to the development of reliable benchmarks for AI training and assessment.

Must-Have Skills

  • Strong expertise in Electrical Engineering and Computer Science, with the ability to evaluate complex technical implementations across relevant engineering domains.
  • Proficiency in C/C++, along with experience in programming languages such as Python, Rust, Julia, Fortran, MATLAB/Octave, or hardware description languages.
  • Advanced proficiency in Linux environments, command-line tools, and terminal-based development workflows.
  • Hands-on expertise in at least one major technical domain, 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 fundamentals.
  • Experience reviewing complex codebases, engineering simulations, technical implementations, or research outputs.
  • Ability to evaluate computational performance, algorithmic correctness, numerical accuracy, concurrency, and system reliability.
  • Strong analytical and problem-solving skills, with the ability to identify technical errors, edge cases, race conditions, and flawed engineering assumptions.
  • Experience leading, mentoring, or reviewing the work of small technical teams.
  • Excellent written communication skills and the ability to provide clear technical feedback and maintain quality documentation.

Good-to-Have Skills

  • Experience with parallel and high-performance computing technologies such as MPI, OpenMP, and CUDA.
  • Familiarity with systems development tools such as LLVM/GCC, CMake, GDB, Valgrind, performance profilers, sanitizers, and Slurm.
  • Experience 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.
  • Exposure to AI coding agents, terminal-based agents, large language models, or AI-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.
  • Previous experience in AI training, technical quality assurance, benchmark development, or evaluation programs.

Education and Experience

  • Ph.D., postdoctoral research experience, or equivalent advanced technical expertise in Computer Science, Electrical Engineering, Computer Engineering, or a closely related discipline.
  • Demonstrated experience leading technical teams and reviewing complex engineering or software deliverables.
  • A strong foundation in scientific computing, systems engineering, electrical engineering principles, or advanced software development.
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