Senior Machine Learning Engineer, Applied Intelligence

What you'll do

• Own the production hardening and deployment of ML prototype applications into reliable, maintainable production systems.
• Build and maintain inference infrastructure across cloud environments and on-premises OT hardware on the factory floor.
• Implement comprehensive monitoring, alerting, and drift detection systems to maintain operational SLOs for production ML applications.
• Translate ML research requirements into concrete infrastructure for datasets, training pipelines, and inference systems.
• Architect the end-to-end ML platform stack including data ingestion, labeling, training orchestration, and model serving.
• Manage dataset curation, versioning, and model governance processes to ensure high-quality model development.
• Participate in on-call rotations to ensure high availability and rapid incident response for deployed ML products.
• Write production-quality code and establish best practices for MLOps, observability, and secure data handling.

What we're looking for

• 6+ years of experience in software engineering or ML engineering roles building production systems.
• Strong production MLOps experience including data pipeline management, model versioning, deployment automation, and production monitoring.
• Proven track record of taking ML models from prototype to production and maintaining them long-term.
• Experience with on-call rotations and production incident management for data or ML systems.
• Strong proficiency in Python and experience with deep learning frameworks like PyTorch or TensorFlow.
• Experience building and deploying containerized ML services using Docker and Kubernetes.
• Solid data engineering skills including pipeline orchestration, data quality management, and working with time-series or event-driven data.
• Degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
• U.S. Person status is required to access export-controlled data.
• Experience in manufacturing, industrial, or OT-adjacent domains (preferred).
• Background deploying ML models to edge devices or on-premises hardware in production environments (preferred).
• Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal (preferred).
• Familiarity with GPU infrastructure management and inference optimization (TensorRT, Triton Inference Server) (preferred).
• Experience with MLOps tools including experiment tracking, feature stores, and model registries (preferred).
• Experience building RAG systems, vector databases, or LLM deployment pipelines (preferred).
• Background with computer vision model deployment, OCR/IDP systems, or document processing pipelines (preferred).
• Experience coordinating with platform or infrastructure teams at larger organizations (preferred).
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration (preferred).
• Knowledge of edge ML deployment, model optimization, or deploying models on resource-constrained devices (preferred).
• Experience in regulated environments (NNPI/ITAR) and secure model/data governance (preferred).
• Experience in hyper-growth startup environments (preferred).
• Familiarity with enterprise systems such as ERP, MES, WMS, or PLM (preferred).
• Eligible to obtain and maintain a U.S. Secret security clearance (preferred).