About our group:
Seagate Research Group (SRG) drives innovation by combining Seagate's deep technical expertise, world-class manufacturing, and cutting-edge research. Our mission is to explore transformative technologies that shape the rapidly growing datasphere.
Within SRG, Applied AI Research team applies advanced Machine Learning (ML) methods to accelerate Seagate's next-generation projects, products, and processes.
About the role - you will:
As a Data Scientist or AI/ML Engineer to build state-of-the-art models and proof-of-concepts. In this role, you will design, implement, and deploy advanced ML solutions. Depending on your expertise and interests, you will focus on one of the following key tracks:
- Scientific ML & Discovery: Novel material discovery at the nanoscale, atomistic-scale ML surrogates, and physics-informed ML for simulation.
- Engineering Optimization: AI-driven engineering design for HDD components and predictive maintenance for performance reliability.
- Systems Architecture: Optimization of data flow, storage architectures, and filesystem optimization (user and kernel space).
About you:
- Master's degree or higher in Computer Science, AI/ML, Applied Mathematics, Physics, or a related field.
- Proficiency in Python and frameworks like PyTorch or TensorFlow. Experience with C/C++ or Java is a plus.
- Strong grasp of Linear Algebra, Probability, Statistics, Optimization, and Calculus.
- ML Expertise: Hands-on experience with Supervised/Unsupervised Learning, Transformers, Generative AI (GANs, VAEs, Diffusion), and Deep Learning.
- Self-motivated, independent learner, and a collaborative problem solver eager to explore emerging technologies.
Your experience includes:
To have depth in at least one of the following areas:
- Scientific Machine Learning (SciML): Experience with Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNO), or DeepONet.
- Generative Design: Using VAEs, GANs, or Diffusion Models for molecular/material structures.
- Graph Neural Networks (GNNs): Applied to structured data, molecules, or complex engineering systems.
- Reinforcement Learning: Applied Q-Learning or Genetic Algorithms for system optimization.
- Systems & Storage: For filesystem-focused candidates-a strong grasp of OS internals and low-level programming (C/C++) is essential.
