Senior Technologist - Machine Learning

As the ML Technical Lead on this team, you will be the person who makes physics-informed AI work in product development, not as a research prototype, but as a deployed system that drives real engineering decisions. Precision product development is one of the last frontiers where AI has not yet been systematically deployed at the physics level.

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 physics-constraint validation and surrogate model pipelines; Architect active learning pipelines and Bayesian experimental design frameworks; Own the full ML platform (MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway, CI/CD, observability, model monitoring); Lead design reviews and code reviews; Provide technical direction to the team and define data interface contracts; 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 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; Track record of shipping ML models into product development; Expert-level Python; PyTorch expertise including custom loss functions and full training loop ownership; Computer Vision (CNN, U-Net, ViT); Anomaly Detection from sensor and time-series data streams; Surrogate Modeling in limited-data scientific regimes; Active Learning and Bayesian Experimental Design; Product Development MLOps (MLflow, Docker, AWS EKS, observability platforms such as LangFuse, PortKey or other LLM gateways)

Nice to have

PINNs; Reinforcement Learning; RAG / GraphRAG; LangFuse / PortKey; RLHF and reward modeling; AWS; LLM Finetuning (LoRA, QLoRA); LLM Distillation; Experience in materials science, semiconductor, or precision product development; Significant open-source ML contributions or public technical writing