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Ai Ml Engineer

  • Job type Posted on: Jul 24, 2026
  • Experience level American IT Systems
  • Employment type Santa Clara, California
  • Employment type Onsite
  • Salary CTC

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Job Title :

Ai Ml Engineer

Job Type :

CTC

Job Location :

Santa Clara California United States

Remote :

No

Jobcon Logo Job Description :

AI/ML Engineer

Santa Clara, CA onsite -Day1

6 Months

AI/ML Model Development

  • Design, develop, and train deep learning models for simulation and predictive analytics applications.
  • Implement machine learning algorithms for data driven modeling, optimization, and forecasting.
  • Develop surrogate models and reduced order models to accelerate complex simulations.
  • Fine tune and optimize neural networks for performance, scalability, and accuracy.

Simulation & Data Processing

  • Process and prepare simulation datasets for machine learning workflows.
  • Integrate simulation outputs with AI/ML models for hybrid modeling approaches.
  • Validate simulation models against experimental or real world data.
  • Develop automated pipelines for simulation data ingestion, preprocessing, feature engineering, and model training.

Model Validation & Deployment

  • Evaluate models using statistical and performance metrics.
  • Conduct sensitivity analysis and uncertainty quantification.
  • Deploy trained models into production or simulation environments.
  • Monitor model performance and continuously improve prediction accuracy.

Jobcon Logo Position Details

Posted:

Jul 24, 2026

Reference Number:

1411-44639

Employment:

CTC

Salary:

Not Available

City:

Santa Clara

Job Origin:

CIEPAL_ORGANIC_FEED

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AI/ML Engineer

Santa Clara, CA onsite -Day1

6 Months

AI/ML Model Development

  • Design, develop, and train deep learning models for simulation and predictive analytics applications.
  • Implement machine learning algorithms for data driven modeling, optimization, and forecasting.
  • Develop surrogate models and reduced order models to accelerate complex simulations.
  • Fine tune and optimize neural networks for performance, scalability, and accuracy.

Simulation & Data Processing

  • Process and prepare simulation datasets for machine learning workflows.
  • Integrate simulation outputs with AI/ML models for hybrid modeling approaches.
  • Validate simulation models against experimental or real world data.
  • Develop automated pipelines for simulation data ingestion, preprocessing, feature engineering, and model training.

Model Validation & Deployment

  • Evaluate models using statistical and performance metrics.
  • Conduct sensitivity analysis and uncertainty quantification.
  • Deploy trained models into production or simulation environments.
  • Monitor model performance and continuously improve prediction accuracy.

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