Top Machine Learning Jobs in Omaha, NE

The Senior Machine Learning Engineer will manage the lifecycle of ML models, transform prototypes to production code, build training pipelines, and monitor system performance.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

The Software Engineer will develop machine learning infrastructure, optimize models, and create tools for data analysis while collaborating with a diverse team.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

As a Senior Machine Learning Engineer, you will drive AI initiatives, collaborate across teams, and develop scalable ML infrastructure to improve healthcare.

In this role, you will design and develop AI and machine learning models, maintain data pipelines, evaluate model performance, and collaborate with cross-functional teams to integrate AI capabilities into web-based applications.

The Staff ML Research Scientist will lead research in deep learning for drug discovery, develop innovative models, and integrate them into commercial software. Responsibilities include mentoring junior researchers and contributing to scientific publications.

The role involves sourcing and recruiting senior engineers in AI, ML, and related fields, building talent pipelines, and collaborating with engineering leadership.

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

The Machine Learning Engineer will design, develop, and deploy ML models using Google Cloud tools, focusing on Document AI and Vertex AI, ensuring scalable applications and efficient performance.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

The role involves improving machine learning ranking models, implementing algorithms, and taking full ownership of machine learning systems within Quora's product.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

As a Senior Machine Learning Engineer, you will design and train ML models for logistics, manage their production deployment, and optimize their performance while collaborating with the data science team.

Lead architecture and engineering for production-grade ML in healthcare: design MLOps CI/CD, scalable low-latency inference for LLMs and classical models, build reliable data pipelines for FHIR/HL7/DICOM, manage cloud IaC and observability, ensure HIPAA/HITRUST compliance, and mentor engineering teams to operationalize agentic AI workflows.

The Senior ML Engineer will design evaluations for AI features, optimize pipelines, and ensure quality in ML systems while collaborating with cross-functional teams.

The role involves deploying and optimizing AI/ML models, integrating them into products, building APIs, and ensuring reliability through testing and monitoring. Candidates will work in a collaborative environment to enhance their MLOps and software skills.

The Machine Learning Operations Engineer develops backend pipelines for deploying ML models, builds Docker containers, manages Kubernetes on GCP, and integrates APIs. Responsibilities include performance monitoring, documentation, and collaboration with software engineers.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

As an ML Platform Engineer, you will enhance the core ML platform's performance and scalability, enabling faster innovation and impactful machine learning solutions.