Staff Engineer - Machine Learning

WD is building the infrastructure behind the AI-driven data economy. The role is one of the engineers on a focused AI team solving hard scientific problems in precision product development. The successful candidate will own two real responsibilities from day one and will be introduced to physics-informed AI, Bayesian methods, and product development ML systems within their first year under direct mentorship from engineers and researchers.

Responsibilities

Experiment tracking and evaluation reporting: own MLflow experiment logging for assigned team model runs, conduct model evaluations using standard metrics, and produce structured evaluation reports; Training data quality validation: validate training datasets with the team through feature distribution checks, label verification, and anomaly flagging; Deep learning model contribution: build and train CNN-based models for image classification and defect detection under team guidance, and contribute to evaluation cycles and training run analysis; ML pipeline contribution: package models in Docker, contribute to CI/CD scripts under guidance, and run inference tests and support deployment validation

Requirements

Bachelor's or Master's degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or related field; Fresh graduate to 1 year of experience; Strong final-year project or thesis with a clear ML component; Python: strong proficiency, clean readable ML code, NumPy/Pandas basics; PyTorch: build, train, and evaluate a basic neural network independently from scratch; CNN architecture basics: implement a basic image classifier and understand 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 and producing a structured evaluation report; Learning mindset: self-directed learning outside coursework and evidence of picking up new concepts quickly

Nice to have

U-Net or ViT exposure (academic project or course sufficient); Uncertainty quantification basics such as Monte Carlo dropout and 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 and entry-level cloud ML deployment