Role & Responsibilities:

- Design realistic, multi-step scientific and engineering tasks based on real-world computational workflows.

- Develop datasets, simulation inputs, geometry files, sensor data, constraints, and configuration files for engineering problems.

- Build expert solutions using Python, R, Julia, Bash, MATLAB/Octave, C/C++, or relevant engineering tools.

- Develop and validate workflows involving simulation, numerical analysis, optimization, control systems, signal processing, and engineering design.

- Create reproducible computational environments with appropriate tools, dependencies, and configurations.

- Develop automated tests and objective grading criteria to validate engineering correctness, physical constraints, units, tolerances, convergence, and numerical behavior.

- Debug computational, solver, dependency, workflow, and performance issues and document assumptions and edge cases.

- Translate authentic engineering workflows into structured tasks for training and evaluating AI agents.

Qualifications Required:

- Ph.D. in Engineering or a closely related field.

- Expertise in one or more areas such as Mechanical, Electrical, Chemical, Aerospace, Civil, Materials, Biomedical, Robotics, Control Systems, or Computational Design.

- Solid programming skills in Python, R, Julia, Bash, MATLAB/Octave, C/C++,



or equivalent languages.

- Hands-on experience with Linux or terminal-based computational environments.

- Experience with engineering simulation, modeling, numerical analysis, optimization, signal processing, control systems, or scientific data analysis.

- Strong understanding of numerical methods, engineering units, physical constraints, boundary conditions, and technical validation.

- Ability to develop, debug, and validate reproducible computational workflows.

Preferred Candidate Profile:

- Experience with scientific libraries such as NumPy, SciPy, pandas, matplotlib, SymPy, or PyTorch.

- Familiarity with engineering tools such as OpenFOAM, CalculiX, FEniCS, ROS, LTspice, QEMU, or equivalent tools.

- Experience with FEM, CFD, robotics, embedded systems, control systems, or digital twins.

- Familiarity with Docker, Git, CI/CD, automated testing, or HPC environments.

- Experience developing technical benchmarks, simulation-based evaluations, automated graders, or programming tasks.

- Experience evaluating AI coding or terminal-based agents or working in research software engineering.

- Publications, patents, open-source contributions, or industry experience in computational engineering.

📌 Scientific Computing (Research Engineering Expert) (Gurugram)
🏢 Crossing Hurdles
📍 Gurugram