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Pod lead - Electrical Engineering & Computer Science; Terminal Bench S

Job in San Antonio, Bexar County, Texas, 78201, USA
Listing for: Zettamine Labs
Full Time, Part Time position
Listed on 2026-11-05
Job specializations:
  • Software Development
    Computer Science, Software Engineer
Salary/Wage Range or Industry Benchmark: 96000 - 179000 USD Yearly USD 96000.00 179000.00 YEAR
Job Description & How to Apply Below
Position: Pod lead - Electrical Engineering & Computer Science (Terminal Bench S

Hello,

Greetings from Zetta Mine!!!

Freelancing Opportunity :

-
We are hiring a Pod Lead Electrical Engineering & Computer Science (Terminal Bench Science) for a short-term Frontier AI Research, Scientific Computing &

Engineering Evaluation

project across the following location:
India

Role: Pod Lead Electrical Engineering & Computer Science (Terminal Bench Science)

Educational Eligibility: PhD, postdoctoral experience, or equivalent advanced technical experience in Computer Science, Electrical Engineering, Computer Engineering, or a closely related discipline

Mode: Remote

Start: Immediate

Employment Type: Part-time

Availability

  • Minimum 40 hours per week
    .
  • Availability to work at least 8 hours per day
    .
  • Minimum 4 hours of overlap with PST business hours
    .

Mandatory Eligibility Criteria

Technical

Qualification:

  • PhD, postdoctoral experience, or equivalent advanced technical expertise in Computer Science, Electrical Engineering, Computer Engineering
    , or a closely related field.
  • 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 | 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 the reliability of technical results.
  • Strong analytical skills to identify technical errors, edge cases, race conditions, and flawed engineering assumptions.
  • Experience mentoring technical teams, reviewing deliverables, and maintaining quality standards.
  • Excellent written communication skills for providing precise technical feedback and documenting engineering requirements.

What Youll Work On

  • Lead and mentor a pod of approximately 510 technical trainers developing realistic, terminal-based scientific and engineering tasks for a frontier AI training and evaluation initiative.
  • 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 accurately evaluate 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 actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
  • Onboard trainers, allocate work, monitor quality and productivity, and resolve technical blockers.
  • Maintain technical documentation, quality standards, and best practices throughout task development.

Thanks & Regards,

Sowmya P

Zetta Mine

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