Senior Technologist - Machine Learning

WD is building the infrastructure behind the AI-driven data economy, combining deep engineering expertise with global-scale manufacturing to deliver storage systems for hyperscale data centers, cloud platforms, and enterprise infrastructure. As the ML Technical Lead on the precision product development team, the role focuses on making physics-informed AI work in product development as a deployed system that drives real engineering decisions, including deep learning systems architecture, physics-informed AI and surrogate modeling, active learning pipeline architecture, MLOps platform ownership, and technical direction of the team.

Responsibilities

Design and own CNN, U-Net, and ViT systems for precision inspection, measurement, and defect classification; Own real-time anomaly detection from sensor and time-series product development data streams; Validate and deploy PINNs methodology, own product development implementation, validate physics constraints against engineering requirements, and own surrogate model pipelines; Architect active learning pipelines and Bayesian experimental design frameworks integrating ARS-designed acquisition functions with DE-supplied feature data and laboratory scheduling systems; Own the full ML platform including MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway (LangFuse/PortKey), CI/CD, observability, and model monitoring; Lead design reviews and code reviews; provide technical direction to the team; define data interface contracts with the DE; partner with domain scientists from problem definition through deployment; mentor junior team members

Requirements

Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or related field (equivalent depth via open-source contributions or significant GitHub portfolio considered); Minimum 3-5 years of hands-on technical experience; Demonstrated technical ownership of AI systems from design to product development deployment; Proven direction of a small ML or AI engineering team with evidence of design reviews, mentoring, and cross-functional delivery; Track record of shipping ML models into product development; Python: Expert-level proficiency; PyTorch: Expert, including custom loss functions and full training loop ownership; Computer Vision: CNN, U-Net, ViT for inspection, defect detection, and measurement; Anomaly Detection: real-time anomaly detection from sensor and time-series product development data streams; Surrogate Modeling: data-efficient ML in limited-data scientific regimes with end-to-end pipeline ownership; Active Learning and Bayesian Experimental Design: acquisition function pipelines and laboratory scheduling and instrument control integration; Product Development MLOps: MLflow, Docker, AWS EKS, observability platforms such as LangFuse or PortKey or other LLM Gateways

Nice to have

PINNs: validate ARS-originated designs and own physics-constrained product development models; Reinforcement Learning; RAG / GraphRAG retrieval-augmented generation pipeline ownership; LangFuse / PortKey LLM Gateway and agent observability; RLHF and reward modeling with domain expert feedback integration; AWS; LLM Finetuning (LoRA, QLoRA); LLM Distillation; Experience in materials science, semiconductor, precision product development, or equivalent scientific/industrial AI; Significant open-source ML contributions, public technical writing, or documented GitHub portfolio