Technical Lead Electrical Engineering and Computer Science
Jobgether • India
Posted: October 27, 2026
Job Description
- This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Technical Lead – Electrical Engineering and Computer Science based in India. Join a technically driven team focused on developing high-quality scientific and engineering tasks for AI training and evaluation. In this leadership role, you will guide a team of 5–10 technical trainers responsible for creating realistic, terminal-based challenges that reflect authentic engineering workflows. You will oversee the entire task development lifecycle, from technical design and implementation to validation, testing, and final approval. Working across computer science, scientific computing, and electrical engineering, you will ensure that each task demonstrates technical accuracy, reproducibility, and meaningful problem-solving complexity. You will also establish quality standards, mentor technical contributors, and improve evaluation methodologies to support reliable AI assessment. This fully remote, full-time opportunity is ideal for an experienced technical leader passionate about engineering excellence, scientific rigor, and advancing AI capabilities through robust technical benchmarks.
- Accountabilities Team Leadership and Mentorship: Lead, mentor, and onboard a team of approximately 5–10 technical trainers. Allocate assignments, monitor productivity, resolve blockers, review performance, and ensure the timely delivery of high-quality technical tasks. Technical Review and Validation: Oversee the complete task lifecycle, reviewing problem statements, codebases, circuit netlists, hardware description languages (HDLs), datasets, computational environments, reference solutions, and automated tests. Scientific and Engineering Task Assessment: 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 and Correctness: 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, reliable, and reproducible task execution. Testing and Evaluation Quality: Review automated tests, graders, and evaluation criteria to ensure they measure 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 and Feedback: Provide precise, actionable technical feedback, track revisions, and ensure identified issues are resolved before tasks receive final approval. Technical Documentation and Standards: Develop and maintain technical guidelines, review criteria, documentation, and best practices that promote consistency, accuracy, and quality across the team. Process Improvement and Benchmark Development: Improve task creation and evaluation workflows, strengthen quality assurance practices, and contribute to reliable benchmarks for AI training and assessment. Cross-Domain Technical Collaboration: Apply deep engineering knowledge to assess complex implementations and ensure that tasks require authentic, multi-step technical reasoning rather than superficial or easily bypassed solutions. Requirements Education and Technical Background: Ph.D., postdoctoral research experience, or equivalent advanced technical expertise in Computer Science, Electrical Engineering, Computer Engineering, or a closely related discipline. Professional Experience: At least 7 years of relevant experience, with demonstrated expertise in technical engineering, scientific computing, systems engineering, electrical engineering, or advanced software development. Technical Leadership: Proven experience leading, mentoring, or reviewing the work of technical teams, including the ability to coordinate assignments, maintain quality standards, and deliver results on schedule. Programming Proficiency: Strong expertise in C/C++, with experience in additional languages such as Python, Rust, Julia, Fortran, MATLAB/Octave, or hardware description languages. Linux and Command-Line Expertise: Advanced proficiency in Linux environments, command-line tools, and terminal-based development and debugging workflows. Specialized Technical Expertise: Hands-on experience 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. Computer Science and Engineering Fundamentals: Strong understanding of algorithms, computational systems, numerical methods, software testing, and electrical engineering principles. Technical Review and Quality Assurance: Experience reviewing complex codebases, engineering simulations, technical implementations, or research outputs, with the ability to identify errors and assess technical validity. Performance and Reliability Analysis: Ability to evaluate computational performance, algorithmic correctness, numerical accuracy, concurrency, system reliability, and edge cases. Analytical and Problem-Solving Skills: Strong critical thinking and troubleshooting abilities, including identifying race conditions, numerical instability, flawed engineering assumptions, and weaknesses in testing methodologies. Communication and Documentation: Excellent written communication skills, with the ability to provide clear technical feedback, document review findings, and establish consistent engineering standards. Collaboration and Ownership: A proactive, detail-oriented approach to technical leadership, with the ability to work independently, resolve complex challenges, and support a distributed team. Preferred Qualifications High-Performance Computing: Experience with parallel and HPC technologies such as MPI, OpenMP, and CUDA. Systems Development Tools: Familiarity with LLVM/GCC, CMake, GDB, Valgrind, performance profilers, sanitizers, and Slurm. Electrical Engineering and Simulation Tools: Experience with tools such as ngspice, Xyce, Icarus Verilog, Verilator, GHDL, Yosys, pandapower, PyPSA, MATPOWER, openEMS, GNU Radio, or QEMU. Software Engineering Practices: Familiarity with Docker, Git, CI/CD pipelines, and automated testing frameworks such as pytest. Benchmark and Evaluation Development: Experience developing technical benchmarks, programming challenges, simulation-based evaluations, or automated grading systems. AI Technologies: Exposure to AI coding agents, terminal-based agents, large language models, or AI-generated technical solutions. Open-Source Contributions: Experience contributing to or maintaining open-source projects, particularly reviewing and validating external contributions. Relevant Industry Experience: Background in semiconductors, high-performance computing, power utilities, telecommunications, embedded systems, or related technical industries. AI Training and Quality Assurance: Previous involvement in AI training, technical quality assurance, benchmark development, or evaluation programs. Benefits Remote Work Flexibility: Work remotely from India in a full-time position. Technical Leadership Opportunity: Lead and mentor a specialized team working across computer science, scientific computing, and electrical engineering. Advanced Technical Challenges: Work on complex engineering tasks and evaluation frameworks designed to improve AI training and assessment. Cross-Disciplinary Exposure: Apply expertise across software systems, computational science, electrical engineering, simulation, and hardware-related domains. Process Ownership: Help establish technical standards, improve validation workflows, and shape the quality of engineering benchmarks. Professional Growth: Deepen expertise in technical leadership, engineering quality assurance, scientific computing, and AI evaluation. Compensation: Salary details were not specified in the original job posting.
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