Simulation Product Engineer (Govindapperi)

Simulation Product Engineer (Govindapperi)

13 Aug
|
Recognized
|
Govindapperi

13 Aug

Recognized

Govindapperi

Join India’s First Native Electromechanical Product Team!



Pilabz Electromechanical Systems (a Zoho Corp.
subsidiary) is India’s first native electro mechanical product company. We
design and manufacture world -class electronic test and measurement instruments
from rural Tamil Nadu. We believe real engineering means building, not just
simulating — and that a focused rural team, given the right tools, can
out -engineer any urban lab.



About Your Role:



- We are seeking a
Simulation Product Engineer (PhD level) to own the development of Multiphysics modelling
capabilities within our Pilabz -Forge CAE platform. The primary focus is
electric motor simulation (IPMSM, SPMSM, SCIM, SynRM) coupled with CFD,
thermal, and structural domains.


- You will work at the intersection of traditional
computational physics (CFD, Thermal, Structural, and Electromagnetic) and
modern Artificial Intelligence. By integrating physics -informed machine
learning (such as PhysicsNeMo) with open -source solvers, you will help us
create the next generation of accelerated design and simulation tools.


- You will couple open -source solvers (OpenFOAM, Gmsh,
Elmer, Pyleecan, OpenCascade) with physics -informed ML (PhysicsNeMo) and
Python/C++ tooling to build the next generation of accelerated electro mechanical design tools inside Pilabz -Forge.


Your Responsibilities:

Multi -Physics Methodology: Formulate and
implement advanced computational models covering Electromagnetic fields,Conjugate Heat Transfer (CHT), Fluid -Structure Interaction (FSI), and
Structural Mechanics for electro mechanical systems.



AI/ML Integration: Implement Neural
Operators and Physics -Informed Neural Networks (PINNs) using PhysicsNeMo to build surrogate models for motor electromagnetic and thermal problems,
targeting at least 10× solver speedup over conventional FEA for design -space
exploration and topology optimization.


Software Architecture & Development: Write
production -grade Python and C/C++ code: proprietary solvers,REST/Python APIs,
and CI -tested automation pipelines. Integrate open -source stacks (Pyleecan,
Gmsh, FEMM, Elmer, OpenCascade) into Pilabz -Forge under version control, with
containerized (Docker) deployment.




Solver Selection & Validation: Evaluate
commercial versus open -source trade -offs. Validate simulation results against empirical data from our internal hardware testing and prototyping facilities.





Mentorship & Leadership: Embody our
core value of "Learning by Doing." Mentor junior engineers and rural talent,translating complex PhD -level theoretical physics into practical,




actionable engineering practices.


Requirements

Education: Ph.D. (or highly equivalent
R&D; experience) in Computational Engineering, Applied Mathematics,
Mechanical/Electrical Engineering, or a closely related field.




Experience: 0 -2 years after PhD or 2
years of relevant experience after M.Tech.




Technical
Qualifications:



Domain
Expertise: Deep mathematical and practical understanding of
Electromagnetic, CFD, Thermal Sciences,and Structural Mechanics.


Programming
Mastery: Solid proficiency in Python and C/C++, with experience building
and maintaining complex computational codebases and deploying them via
cloud/containerized architectures.

AI
for Physics: Demonstrated experience with PhysicsNeMo, NVIDIA Modulus,
DeepXDE, or equivalent AI/ML frameworks designed for scientific computing and
PDE solving.

Software
& Tools Proficiency: We embrace an ecosystem of flexible open -source
frameworks. Candidates should be comfortable navigating and integrating tools
across these categories either with the Open -Source Stack or with commercial equivalents.



Domain



Open -Source Stack



Commercial Equivalents



Electromagnetics



Pyleecan, FEMM, Elmer, Gmsh



Ansys Maxwell, Motor -CAD, JMAG



CFD & Thermal



OpenFOAM, SU2



Ansys Fluent, STAR -CCM+, COMSOL



Structural & CAD



FreeCAD, CalculiX, FEniCS



Ansys Mechanical, Abaqus



AI/ML & Scripting



PhysicsNeMo, PyTorch, TensorFlow



Python, C/C++





























Preferred Skills:

- Experience with motor design workflows including
winding configuration,



slot -pole analysis, and loss decomposition (copper,
iron, magnet, mechanical)

- Familiarity with model order reduction (MOR)
techniques for real -time simulation or hardware -in -the -loop (HIL) environments.

- Exposure to power electronics co -simulation
(e.g., inverter -motor coupled models) and experience with tools such as PLECS,
PSIM, or Simulink.

- Working knowledge of version control (Git),
CI/CD pipelines, and containerization (Docker/Kubernetes) for simulation
software deployment.

- Published research, conference papers, or
open -source contributions in computational physics, scientific ML, or electro mechanical design.

- Experience with HPC environments,
GPU -accelerated solvers (CUDA/OpenCL), or distributed computing frameworks for
large -scale simulation workloads.


Mandatory Portfolio Requirements (Proof of Skills):We do not accept theoretical experience. Candidates
must provide the following evidence:



Core Competencies:



First Principles Thinking: When a solver
diverges, a mesh fails, or a surrogate model gives nonsense, you debug from
Maxwell’s equations and the Navier -Stokes equations up—not from documentation
down.


Independence: No simulation validation
process exists here yet. No workflow standards. You will write them. You are comfortable making technical decisions with incomplete information and owning
the outcome.



Adaptability: This week’s best
open -source solver may be replaced next month. You follow physics, not the
tool and you bring your team with you when the stack changes.





Benefits

Benefits & Culture
at Pilabz:


- Impactful Work: Directly contribute
to the Pilabz -Forge product roadmap and our physical electro mechanical instrument line. Your simulation models will inform real motor designs that go
into production hardware built on - site.

- Purpose -Driven Environment: We are
building an engineering culture from scratch in Govindaperi — a village near
Tenkasi — proving that PhD -level R&D; does not require a metro address. Your
presence and mentorship directly shape what that culture becomes.

- Continuous Learning: Access to Zoho’s
R&D; resources, internal hardware prototyping facilities, and a team that
treats every failed simulation run as a research question worth solving
properly.




📌 Simulation Product Engineer (Govindapperi)
🏢 Recognized
📍 Govindapperi

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