SUMMARY

The R&D Engineer II is responsible for the development of software products and supporting systems. As part of Office of CTO, in this role you will do research using deep learning techniques such as state of the art vision transformer models or similar and implement them on our local ML pipeline. This will be towards building scalable foundation models for physics.

MINIMUM QUALIFICATIONS

• BS in Engineering, Computer Science, or related field.
• Working experience with Deep Learning specifically with vision transformers and diffusion models.
• Expertise in C++ or Python with focus on deep learning frameworks like TensorFlow, PyTorch, JAX, or Scikit Learn

PREFERRED QUALIFICATIONS

• Masters or PhD with software development experience in working with SLM/LLMs and fine tuning.
• Publication in leading journals or conferences such as, NeurlPS, ICML or ICLR
• Experience with 3D geometry or computer vision
  At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement

Our Commitments:

• Amaze with innovative products and solutions
• Make our customers incredibly successful
• Act with integrity
• Ensure employees thrive and shareholders prosper

Our Values:

• Adaptability: Be open, welcome what’s next
• Courage: Be courageous, move forward passionately
• Generosity: Be generous, share, listen, serve
• Authenticity: Be you, make us stronger

Our Actions:

• We commit to audacious goals
• We work seamlessly as a team
• We demonstrate mastery
• We deliver outstanding results

• Research and prototype vision tranaformer models with ideas from LLMs and generative diffusion models to apply them for specific physics and physics models.
• Build and curate large datasets and models with fundamental general capabilities of different modalities of physics such understanding of specific physics, boundary conditions, loading as well as other constraints in physics.
• Research deep learning methods in context of fluids, structures, electromagnetics, optics, and electronics.
• Participates in brainstorming, planning, architecture and research.
• Collaborate with product engineering teams to drive real-time model implementations and new feature creations.
• Design efficient model pipelines to deploy foundation models.
• Publish influential research outcomes, and give presentations in top-tier conferences and journals.
• Works independently with minimal supervision and may take on some planning and mentoring responsibilities.