Staff Engineer - Machine Learning

WD is building the infrastructure behind the AI-driven data economy.

As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in.

We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.

This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today.

We’re looking for people who want to build, solve, and operate at that level.

Join us and let’s shape the future of data.

About This Role - The Mission

You will be one of the engineers on a focused AI team solving hard scientific problems in precision product development. You will own two real responsibilities from day one - not toy tasks, but actual team workflow contributions that senior engineers depend on. You will be introduced to physics-informed AI, Bayesian methods, and product development ML systems within your first year under direct mentorship from engineers and researchers who have worked at world-class institutions. If you are the kind of person who learns fast and wants to be in the middle of hard problems early in your career, this is an unusual opportunity.

Key Responsibilities

• Experiment Tracking & Evaluation Reporting: Own MLflow experiment logging for assigned team model runs; conduct model evaluations using standard metrics; produce structured evaluation reports reviewed by team. Your reports directly inform model iteration decisions.
• Training Data Quality Validation: Validate training datasets jointly with team - feature distribution checks, label verification, anomaly flagging. Your quality flags are the final check before data enters the model training pipeline. You close the data quality loop between workstreams.
• Deep Learning Model Contribution: Build and train CNN-based models for image classification and defect detection under team’s guidance. Contribute to model evaluation cycles, configuration comparisons, and training run analysis.
• ML Pipeline Contribution: Package models in Docker; contribute to CI/CD scripts under guidance; run inference tests and support deployment validation in product development environments.

Requirements

Education:

• Bachelor's or Master's degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or related field. AI major or strong research focus in degree strongly preferred.

Experience:

• Fresh graduate to 1 year. Work experience not required - demonstrated ML project competency is the primary criterion. Strong final-year project or thesis with a clear ML component; research internship preferred.

Must Have Skill:

• Python: Strong proficiency - clean, readable ML code; NumPy/Pandas basics
• PyTorch: Foundational - build, train, and evaluate a basic neural network independently from scratch
• CNN Architecture Basics: Understand and implement a basic image classifier; conceptual understanding of convolutional layers
• Surrogate Modeling Concepts: Why data-efficient ML matters in limited-data scientific settings
• Active Learning Awareness: Conceptual understanding of uncertainty-guided data selection
• MLflow Basics: Log experiments, parameters, and metrics for a training run
• Docker Basics: Write a Dockerfile to containerize a Python/ML application
• Model Evaluation: Standard metrics; produce a structured evaluation report
• Learning Mindset: Self-directed learning outside coursework; evidence of picking up new concepts quickly.

Good-to-Have Skill:

• U-Net or ViT exposure - academic project or course sufficient
• Uncertainty quantification basics - Monte Carlo dropout, ensemble methods
• Bayesian methods introduction - any probabilistic ML course or project
• Time-series or sensor data - any project with sequential or temporal data
• RL introduction - any RL course or gym environment experiment
• RAG basics, any LLM project
• AWS fundamentals; entry-level cloud ML deployment

#LI-FN1

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