Top Hybrid Machine Learning Engineer Jobs

The AI Intern supports Hoosier Energy in leveraging artificial intelligence for efficiency by developing AI models, preparing datasets, and ensuring compliance with data governance.

Embed with strategic customers to own end-to-end delivery of robotics/egocentric data products: translate requirements, sequence and QA deliveries, triage technical issues, and feed recurring needs into the data-engine and annotation roadmaps to improve model performance.

Own and ship full-stack features across frontend, backend, and data layers. Extend a Brand Knowledge Graph in Neo4j, build predictive optimization and measurement systems, design LLM orchestration and AI integrations to minimize calls, and deliver client-facing demos and QA. Translate complex data and models into actionable insights and maintain integrations and tracking infrastructure.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Conduct R&D on AI/ML and machine translation for SIGINT and cybersecurity applications. Build research prototypes, evaluate and enhance MT tools and text-processing pipelines, and implement containerized solutions. Resolve complex AI technical problems and collaborate with senior technical staff.

Design, train, and deploy multimodal machine learning systems that fuse 3D/vision, simulation, tabular, and text data. Own end-to-end model lifecycle from architecture and training to evaluation, production deployment, and monitoring, and build agentic workflows and retrieval/embedding capabilities to support underwriting and mitigation decisions.

As a Machine Learning Engineer, you will design and train perception models for robotics, focusing on 3D perception systems. You will tackle complex physical challenges, optimize models for embedded hardware, and collaborate with cross-functional teams.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Design, build, and deploy machine learning models to detect fraud, prevent losses, and monitor risk. Work with Product, Engineering, and Operations to turn large-scale data into production-ready pipelines and model-serving solutions, monitor performance, and continuously improve model accuracy and business impact.

As an AI/ML Engineer, you will develop and optimize AI solutions for renewable energy assets, collaborating with a skilled team to deploy algorithms in real-world projects.