Top Hybrid Machine Learning Engineer Jobs

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.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Research, design, and implement discrete optimization and mission-planning algorithms for multi-agent autonomous systems; improve and maintain C++ mission-planning software; define technical roadmaps; mentor engineers; perform full software and hardware-in-the-loop simulations.