Senior AI/ML Engineer

Seagate International Headquarters Pte. Ltd.; Otago; On-site; NZD 120,000 - 170,000; Full time

Job summary

Seagate Research Group in New Zealand seeks a Data Scientist or AI/ML Engineer to build state-of-the-art ML models and proofs-of-concepts. You will design, implement, and deploy advanced ML solutions, focusing on Scientific ML, Engineering Optimization, or Systems Architecture.

Collaborating with physicists, material scientists, and firmware engineers, you will accelerate Seagate’s next-generation projects and processes through rigorous experimentation and scalable deployment.

Qualifications

• PhD or Master’s degree in Computer Science, AI/ML, Applied Mathematics, Physics, or a related field.
• Strong Python and ML framework skills (PyTorch or TensorFlow).
• Experience with linear algebra, probability, statistics, optimization, and calculus.

Responsibilities

• Build state-of-the-art ML models and proofs-of-concept.
• Design, implement, and deploy advanced ML solutions.
• Collaborate with physicists, material scientists, and firmware engineers to solve complex problems.
• Explore one of three tracks: Scientific ML, Engineering Optimization, Systems Architecture.

Skills

Python

ML/DL

PyTorch

TensorFlow

C/C++

Java

Reinforcement Learning

Education

PhD or Master’s in CS/AI/Applied Math/Physics

Tools

Python frameworks (PyTorch, TensorFlow)

Job description

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.