Engineer the Future with Us

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Engineering · Software Engineering

In short

Synopsys seeks a Senior Engineer in Pune to build and scale ML systems for scientific simulation data. This role involves creating data factories, ensuring data quality, and managing model deployment into production workflows for physics-informed ML products.

Responsibilities

Build and extend dataset factories for repeatable, versioned corpora using agentic orchestration for CFD, Icepak, and HFSS.
Produce large, quality-gated datasets for training Large Physics Foundation Models, tracking data provenance.
Manage model checkpoint deployment, including packaging, inference serving, and product team integrations.
Partner with product teams to understand deployment constraints and test models in real workflows.
Write production-grade Python code with testing, versioning, CI, and operability for data generation, training, and serving jobs.
Build data loaders and training pipelines for irregular 3D simulation data using PyTorch, handling distributed training and physics-based evaluation.
Own the boundary between data engineering, model training, and product deployment.

Requirements

Master's degree in Mechanical Engineering, Aerospace Engineering, Electrical Engineering, Applied Mathematics, Physics, or a related field.
2-3+ years of experience building ML systems in production.
Meaningful experience with large scientific datasets, simulation data, HPC environments, or deploying models beyond notebooks.
Strong Python engineering practices: testing, versioning, CI/CD, API design, and operability of long-running jobs.
Hands-on deep learning experience with PyTorch or equivalent, including data loaders for irregular 3D data, distributed training, and checkpointing.
Demonstrated ability to own work across ambiguous boundaries between data engineering, model training, and product deployment.

Desired Qualifications

PhD is welcome but not required.
Experience with transformers, vision-language models, neural operators, geometric deep learning, graph networks, or physics surrogates is a strong plus.
Ability to explain technical tradeoffs to product managers and debug distributed training failures.
Systems thinking approach, considering packaging, serving, versioning, and supportability.

Benefits

Comprehensive range of health, wellness, and financial benefits.
Total rewards include both monetary and non-monetary offerings.

Skills

PythonPyTorch

#ML#Machine Learning#Python#PyTorch#Data Engineering#Production#Deployment#HPC#Scientific Datasets#Simulation Data

About Synopsys Inc

Smart, Secure Everything—From Silicon to Software Synopsys technology is at the heart of innovations that are changing the way we live and work. The Internet of Things. Autonomous cars. Wearables. Smart medical devices. Secure financial services. Machine learning and computer vision. These breakthroughs are ushering in the era of Smart, Secure Everything―where devices are getting smarter, everything’s connected, and everything must be secure. Powering this new era of technology are advanced silicon chips, which are made even smarter by the remarkable software that drives them. Synopsys is at…

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