Principal Engineer - Machine Learning

Requirements

Education:

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

Experience:

• 1–3 years of hands-on ML engineering experience, or equivalent depth demonstrated through internships, academic research, or open-source contributions. Must show component-level technical ownership within an end-to-end ML pipeline (training through deployment) — not just execution under direction. Kaggle rankings, arXiv preprints, or significant open-source ML contributions are valued as evidence of depth.

Must have Skills:

• Python: Strong proficiency — primary ML development language
• PyTorch: Proficient → 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. Candidates with depth across multiple areas (e.g. full inspection-scope coverage — segmentation, transformer vision, and anomaly detection together) will be considered for the higher end of the band.
• Surrogate Modeling: Implement and iterate surrogate pipelines under P110 architectural guidance
• Active Learning: Configure acquisition functions; uncertainty-guided experiment scheduling
• Data-to-Model Interface: Define feature specs; validate incoming datasets against model requirements; flag data quality issues before training
• Practical MLOps: MLflow, Docker, Git, basic CI/CD contribution
• Model Evaluation & Uncertainty Analysis: Ablation studies, confidence calibration, validation methodology
• Technical Documentation: Model design decisions and evaluation results to production-handoff standard

Good to have Skills:

• PINNs implementation under technical guidance
• Bayesian methods — Bayesian neural networks, Gaussian processes, ensemble uncertainty, calibration
• Reinforcement learning basics — gym environments, policy gradient concepts
• AWS fundamentals — S3, EC2, SageMaker basics; entry-level cloud ML deployment
• RAG pipeline fundamentals

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