Senior AI/ML Engineer

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:

We are looking for 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:

• Innovate: Develop ML/DL models to solve complex physical engineering and system problems.
• Simulate: Build surrogate models to accelerate computationally expensive Finite Element simulations.
• Optimize: Apply Reinforcement Learning or evolutionary algorithms to engineering design and storage systems.
• Collaborate: Bridge the gap between domain experts (physicists, material scientists, firmware engineers) and AI implementation.
• Mindset: A self-motivated and independent learner who collaborates effectively, solves problems creatively, and is eager to explore emerging technologies.

Your experience includes:

• Education: PHD/Master's degree in Computer Science, AI/ML, Applied Mathematics, Physics, or a related field.
• Tech Stack: Proficiency in Python and frameworks like PyTorch or TensorFlow. Experience with C/C++ or Java is a plus.
• Mathematical Foundation: Strong understanding of linear algebra, probability, statistics, optimization, and calculus.
• ML Expertise: Hands-on experience in relevant areas such as supervised and unsupervised learning, deep learning, transformers, or generative AI-including GANs, VAEs, and diffusion models.

• Candidates are expected to have depth in at least one of the following areas:

  • Scientific Machine Learning (SciML): Experience with physics-informed neural networks (PINNs), neural operators such as Fourier neural operators (FNOs), or DeepONet.
  • Generative Design: Using VAEs, GANs, and Diffusion Models for molecular/material structures.
  • Graph Neural Networks (GNNs): Applied to structured data, molecules, or complex engineering systems.
  • Optimization and Reinforcement Learning: Experience applying reinforcement learning, Bayesian optimization, evolutionary algorithms, or related techniques to engineering or systems optimization.
  • Systems and Storage: For candidates specializing in systems architecture, strong knowledge of operating-system internals and low-level programming in C or C++ is essential.