Top Hybrid Machine Learning Engineer Jobs

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Analyze and evaluate machine learning algorithms, develop advanced algorithms for modeling, and collaboratively transition them to software applications, while engaging with stakeholders and researching innovative solutions.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Develop ML and multimodal AI systems that integrate neurophysiological, behavioral, and sensor data for cognitive state estimation and decision support. Build algorithms and prototypes, apply cognitive science to improve explainability and human-AI collaboration, contribute to proposals/publications, and demonstrate capabilities for mission-driven R&D.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

Build and operate production machine-learning systems, including LLM and generative AI applications, scalable APIs, MLOps pipelines, model observability, prompt management, vector search, and real-time or batch inference. Develop Python and SQL backend services using FastAPI, asynchronous workflows, cloud platforms, and containerized deployment. Integrate Azure OpenAI and multimodal models while supporting security, cost optimization, testing, incident management, and client-facing consulting engagements.

Lead development, deployment, and monitoring of underwriting, risk, fraud, and payments ML models. Own full modeling lifecycle, feature engineering, validation, A/B testing, cross-functional collaboration, and mentor junior data scientists.