Principal Engineer - Machine Learning

Wisconsin Development (WD) is building the infrastructure behind the AI-driven data economy. As AI scales, so does data. Every interaction, every model, and every system generates data that must be stored, managed, and made accessible over time. We deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide by combining deep engineering expertise with global-scale manufacturing.

We are looking for people who want to build, solve, and operate at this challenging level to shape the future of data.

About This Role — The Mission

This role is distinct from typical ML engineering positions where work often blends into a pipeline touched by many engineers. Here, you will be the primary owner of critical ML systems. Your focus will be on building systems that detect product development defects, model material behavior using limited data, and select the highest-value experiments from an active learning pipeline. Your models will run directly in product development, and your decisions will have immediate impact.

Key Responsibilities

Deep Learning Model Implementation & Product Ownership

• Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement.
• Own model performance end-to-end, including ablation studies, confidence calibration, and product development performance monitoring.

Anomaly Detection Systems

• Build and maintain real-time anomaly detection for product development sensor and time-series data streams, covering statistical baselines, threshold calibration, and drift alerting.
• Serve as the sole implementation owner for this workstream.

Surrogate Modeling & Active Learning Operations

• Own the implementation and iteration of surrogate model pipelines and active learning systems under the Technical Lead’s architectural direction.
• Configure acquisition functions and integrate systems with versioned feature sets.

Data-to-Model Interface Ownership

• Own the data contract between the Data Engineer and the ML model stack.
• Define feature specifications, validate datasets against model input requirements, and escalate data quality issues before they reach the training pipeline.

MLOps Maintenance & Reliability

• Maintain model versions, training pipelines, and containers following the platform architecture.
• Contribute to MLflow tracking, CI/CD processes, product development monitoring, and degradation escalation.

Mentorship & Documentation

• Provide code review guidance to team members.
• Document model design decisions and evaluation outcomes to production-handoff standard.

Qualifications

Required Qualifications

Education

• Bachelor's or Master's degree in AI, Machine Learning, Computer Science, or a related field. An AI major or strong AI research focus is preferred.

Experience and Skills

• 1–3 years of hands-on ML engineering experience, or equivalent depth demonstrated via internships, academic research, or open-source contributions.
• Must demonstrate component-level technical ownership within an end-to-end ML pipeline (from training through deployment), rather than just execution under direction.
• Prior achievements like Kaggle rankings, arXiv preprints, or significant open-source ML contributions are valued as evidence of depth.
• Programming & Core Skills:
  • Python: Strong proficiency (primary ML development language).
  • PyTorch: Proficient to Expert (independent model training and evaluation).
  • Computer Vision: Strong foundation in CNNs, plus hands-on depth in at least one of: U-Net/segmentation, ViT/transformer-based vision, or time-series anomaly detection.
• ML Systems Expertise:
  • Surrogate Modeling: Ability to implement and iterate surrogate pipelines under architectural guidance.
  • Active Learning: Experience configuring acquisition functions and scheduling experiments guided by uncertainty.
  • Data-to-Model Interface: Skills in defining feature specs, validating incoming datasets, and flagging data quality issues before training.
  • Practical MLOps: Familiarity with MLflow, Docker, Git, and basic CI/CD contributions.
  • Model Evaluation & Uncertainty Analysis: Experience with ablation studies, confidence calibration, and validation methodology.
  • Technical Documentation: Ability to document model design decisions and evaluation results to a production-handoff standard.

Preferred Qualifications (Good to have)

• Implementation experience with PINNs under technical guidance.
• Knowledge of Bayesian methods (Bayesian neural networks, Gaussian processes, ensemble uncertainty, calibration).
• Familiarity with Reinforcement Learning basics (gym environments, policy gradient concepts).
• AWS fundamentals (S3, EC2, SageMaker basics) or entry-level cloud ML deployment experience.
• RAG pipeline fundamentals.

Commitment to Diversity and Inclusion

WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect, and contribution.

WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and hiring process. Please contact [email protected] to advise us of your accommodation request, including a description of the specific accommodation requested and the job title and requisition number.

Notice to Candidates: WD and its subsidiaries will never request payment as a condition for applying or receiving an offer of employment. Should you encounter any such requests, please report it immediately to the WD Ethics Helpline or email [email protected].