Staff Engineer - Machine Learning

Company Overview

WD develops essential infrastructure underpinning the expanding AI-driven data economy. As artificial intelligence grows, so too does the volume of data generated from every interaction, model, and system. WD specializes in delivering robust storage solutions that sustain AI by managing, storing, and providing access to this vast data scale across hyperscale data centers, cloud services, and enterprise environments globally.

This role offers the chance to work on tangible, large-scale systems, tackling some of today’s most challenging technological problems. Individuals eager to innovate, solve complex issues, and operate at a high technical level are encouraged to join and contribute to shaping the future of data.

Role Mission & Responsibilities

As a core engineer on a focused AI team, you will engage with complex scientific challenges in precision product development. From the outset, you will take ownership of meaningful tasks that senior engineers rely on. Over your first year, you'll gain hands-on experience with physics-informed AI, Bayesian approaches, and product-focused machine learning workflows, all supported by mentors with backgrounds in prestigious research organizations.

• Maintain MLflow experiment tracking by logging parameters, metrics, and outcomes for team models, and perform evaluations using established metrics to generate comprehensive reports guiding model iterations.
• Validate training datasets collaboratively by examining feature distributions, verifying labels, and identifying anomalies, thus ensuring data quality before training pipelines intake datasets.
• Develop and train convolutional neural network models for tasks like image classification and defect detection, contributing to evaluation cycles, comparative analyses of configurations, and training run assessments under team guidance.
• Assist with ML pipeline development by containerizing models using Docker, supporting continuous integration and deployment scripts, conducting inference testing, and validating model deployment in product environments.

Required Qualifications

• Bachelor’s or Master’s degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or relevant fields, with a preference for AI majors or research-focused backgrounds.
• Experience level ranges from fresh graduates to candidates with up to one year of experience; prior work experience is not mandatory but demonstrated project capability in ML is critical, emphasizing strong final-year projects or theses and preferably research internships.

Essential Skills

• Proficient Python programming skills, including writing clean and maintainable ML code and familiarity with NumPy and Pandas libraries.
• Foundational PyTorch knowledge allowing independent construction, training, and evaluation of basic neural networks.
• Understanding of CNN architectures with the ability to implement basic image classifiers and conceptualize convolutional layers.
• Awareness of surrogate modeling principles emphasizing data-efficient machine learning in contexts with limited data.
• Conceptual knowledge of active learning methods involving uncertainty-driven data selection.
• Basic experience with MLflow for experiment logging of parameters and metrics.
• Fundamentals of Docker for containerizing Python and ML applications through writing Dockerfiles.
• Capability in model evaluation using standard metrics and formalized evaluation report generation.
• A demonstrated proactive learning attitude showing quick assimilation of new concepts beyond formal coursework.

Desirable Skills

• Exposure to U-Net or Vision Transformer (ViT) architectures through academic projects or coursework.
• Basic understanding of uncertainty quantification techniques such as Monte Carlo dropout and ensemble methods.
• Introduction to Bayesian machine learning methods from relevant courses or projects.
• Experience working with time-series or sensor data involving sequential or temporal data analysis.
• Fundamental knowledge of reinforcement learning including coursework or experimentation in gym environments.
• Basic understanding of Retrieval-Augmented Generation (RAG) or projects involving large language models.
• Entry-level familiarity with cloud computing on AWS and ML deployment on cloud platforms.

Additional Information

WD values diversity as a core strength and is dedicated to creating an inclusive workplace where different perspectives converge to optimize outcomes for employees, the company, customers, and communities. The company is committed to providing equitable opportunities, including accommodations for applicants with disabilities throughout the hiring process. Candidates requiring accommodations can contact WD's job accommodations team with details of their requests.

Applicants should be wary of any fraudulent requests for payment related to job applications or offers, which WD explicitly forbids. Suspected scams should be reported promptly through WD’s Ethics Helpline or compliance email.