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 10Ã 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: Strong 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.
Apply Now
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