Top Machine Learning Engineer Jobs

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

This role involves optimizing inference performance, managing high-performance systems, scaling models, and full-cycle ownership from research to production in a fast-paced environment.

As an Open-Source Machine Learning Engineer at Hugging Face, you'll improve ML libraries, work with the community, and contribute to open-source projects.

Design, train, test, deploy, and maintain ML models for national-security cyber applications. Collaborate with software engineers and data scientists to build LLM-based automation, fine-tune open-source models, containerize solutions, and define ML strategy for mission-critical systems while meeting security requirements.

Design, train, test, deploy, and maintain ML models for defense customers. Collaborate with data engineers, scientists, architects, and product owners to implement deep learning, computer vision, and generative AI solutions and guide AI strategy for mission-critical systems.

Design, build, and deploy production-ready machine learning models for cyber threat detection and hunting. Analyze security logs (endpoint/network), reduce false positives, integrate ML into CI/CD pipelines, and leverage cloud and streaming data platforms to accelerate near real-time detection and defense.

Design, train, test, deploy, and maintain machine learning models for defense-focused solutions. Collaborate with data engineers, data scientists, and product teams to implement deep learning, computer vision, and generative AI use cases and support clients across the DoD market. Maintain model development and data handling pipelines.

The AI / Machine Learning Engineer will design, build, and deploy ML models for image classification, fraud detection, and data pipelines, while collaborating with product and engineering teams.

The Machine Learning Engineer will design, deploy, and maintain ML models, work with large datasets, and collaborate with teams to enhance AI solutions.

The Staff Machine Learning Engineer will design, code, and deploy ML models, improve backend systems, and mentor engineers.

As a Machine Learning Engineer at Hive, you will prototype, train, and deploy deep learning models, analyze results, and collaborate with cross-functional teams to implement machine learning solutions in production.

The Staff Machine Learning Engineer will implement ML models, design neural networks, maintain scalable code, and lead cross-functional collaborations in AI development.

Develop deep learning models for autonomy applications, implement ML algorithms for self-driving vehicles, and resolve real-world testing issues.

Build and maintain enterprise-scale ML/LLM infrastructure on Azure: create automated, secure CI/CD pipelines, provision resources with Terraform, enable real-time inference and RAG pipelines, and collaborate with data science and product teams to productionize generative AI solutions.

The Staff AI/ML Engineer will lead the development of AI systems for regulatory compliance, focusing on ML architecture, implementation, and oversight of model reliability.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Design and implement reinforcement learning algorithms for robotics, conduct experiments, optimize models, and collaborate with cross-functional teams.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

Design, build, and deploy machine learning solutions. Collaborate with teams to transform research into practical applications using large datasets.

Design and implement scalable backend services and distributed, event-driven data pipelines. Build, deploy, and monitor production ML models (feature engineering through deployment). Integrate ML models with rule-based decision engines and contribute to AI/LLM-driven orchestration. Collaborate across engineering, data, and product teams to deliver reliable, production-grade systems.

The Senior Vision AI Engineer will develop and deploy computer vision and machine learning models, create optimized vision pipelines, and mentor junior engineers, focusing on industrial applications.

Lead inference acceleration and GPU-parallelism optimizations (TP, SP, PP); implement high-performance CUDA/NCCL kernels; deploy videogen and LLM models to production; collaborate with researchers, conduct code reviews, mentor engineers, and optionally improve training efficiency and resource utilization.

Develop, train, and deploy AI surrogate models to enhance engineering simulations for aerospace applications while collaborating with engineers to tackle complex challenges.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.