ML & Computational Modeling Scientist – Technical Parts Focus (Remote)

3 weeks ago

India TripleChoice Corp Full time

Role Overview:

We are seeking a motivated ML and Computational Modeling Scientist to develop advanced surrogate and physics-informed machine learning models focused on technical parts such as pumps, compressors, motors, sensors, medical devices, electronics, and many more. This role requires working across hundreds of different parts and systems, so the candidate must be comfortable with diversity and excited about building applications that can impact millions of people globally.

You will play a key role in identifying and extracting physics first principles from literature, collaborating closely with domain experts and AI specialists to build, program, and test integrated physics-based models. Leveraging AI tools and research, you will help uncover governing equations and physical relationships, develop surrogate and machine learning models to enable fast, accurate predictions even with incomplete data, and implement rigorous validation protocols to ensure model reliability before integrating them into our scalable platform.

Role Focus:

  • Develop surrogate and reduced-order models specifically for a wide range of technical parts in mechanical, electronic, and medical domains.
  • Apply machine learning and physics-informed modeling to boost computational speed and fill data gaps.
  • Collaborate with domain experts and use AI tools to identify governing equations and calibrate models.

Key Responsibilities:

  • Identify and extract physics principles from scientific literature and collaborate with team members to translate these into computational models.
  • Develop, program, and test physics-based and surrogate models for hundreds of diverse technical parts and engineering systems.
  • Use AI and machine learning techniques to accelerate model predictions and fill data gaps while maintaining physical fidelity.
  • Implement validation frameworks to verify model accuracy and robustness against real-world data.
  • Integrate validated models into the existing platform to support design, selection, and optimization workflows.
  • Work closely with multidisciplinary teams—including physicists, engineers, data scientists, and AI specialists—to continuously improve model performance and platform capabilities.

Qualifications:

  • Master's or PhD in Computational Physics, Applied Mathematics, Engineering, Computer Science, or related fields.
  • Strong background in surrogate modeling, numerical methods, and machine learning.
  • Proficiency in Python and/or C++.
  • Experience with physics-informed ML or hybrid computational-AI modeling is a plus.
  • Knowledge of parallel computing, GPU acceleration, or HPC frameworks is desirable.
  • Excellent analytical skills and ability to collaborate in a remote, interdisciplinary team.
  • Comfortable working on a diverse set of technical parts and excited by applications impacting millions of users.
  • We welcome applications from recent graduates with strong computational modeling, surrogate modeling, or AI/ML skills who are eager to grow in a fast-paced environment.

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