Machine Learning Engineer

Applied Materials•Santa Clara, CA

11h•Onsite

About The Position

We are a passionate, cross-functional team at the forefront of applying cutting-edge AI and machine learning to accelerate scientific and materials innovation. Our mission is to create domain-specific, product-centric algorithmic solutions that drive real impact for our customers. We thrive in a collaborative environment that encourages out-of-the-box thinking and values diverse perspectives. Here, creativity flourishes—groundbreaking ideas are born from the synergy of technical expertise and open-minded teamwork. We believe the best solutions emerge when everyone is empowered to share their unique insights and challenge conventional boundaries. Our team applies modern generative AI and large language models to tackle complex problems in materials science, scientific discovery, and hardware design. We work closely with scientists, engineers, and product leaders to translate frontier methods into practical, high-value applications. We’re looking for a seasoned machine learning engineer with a deep ML foundation who has actively kept pace with the field—someone equally comfortable with classical ML and the latest generative methods. If you love turning hard problems into working algorithms and shipping them into real products, join us.

Requirements

Strong foundation in machine learning and deep learning, with hands-on experience developing algorithms using modern generative methods and LLMs.
A track record of keeping current: comfort moving from classical ML/optimization into newer architectures and methods as the field evolves.
Experience adapting, and evaluating models for real applications.
Proficiency in Python and frameworks such as PyTorch or TensorFlow.
Excellent communication skills and the ability to collaborate across disciplines.
MS or Ph.D. in Computer Science, Computer/Electrical Engineering, Mathematics, Statistics, or a related field—or equivalent industry experience developing and shipping ML algorithms.

Responsibilities

Design, develop, and adapt generative algorithms to solve concrete problems in process engineering, materials discovery, and hardware design.
Fine-tune, adapt, and optimize models for downstream workflows.
Build robust evaluation protocols, benchmarks, and validation pipelines to ensure models are accurate and trustworthy for scientific use cases.
Translate published methods and frontier techniques into production-ready algorithmic solutions, balancing model quality with practical constraints.
Collaborate with scientists, engineers, and product teams to identify high-impact applications of generative AI.
Build and curate scientific datasets for model development and continuous improvement.
Stay current with advances in the field and bring promising techniques into the team’s work.
Mentor team members and contribute to a collaborative, inclusive engineering culture.