Top Hybrid Machine Learning Engineer Jobs

As a Principal AI Engineer, you'll design scalable AI systems, mentor teams, develop large-scale LLMs, and optimize AI-driven cybersecurity solutions.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

The Research Engineer will develop scalable infrastructure for machine learning research, optimize workflows, and enhance model inference for trading systems.

The Machine Learning Engineer will develop ML models for quality assurance in additive manufacturing, collaborate with engineering teams, and deploy solutions in production.

Lead research, design, and deliver Pearson's scalable learner model and AI-driven features. Partner with product, engineering, and design to define data requirements, build prototypes, maintain data pipelines, ensure data quality, and translate research into production ML capabilities and business growth.

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

Drive post-training experiments to improve agent performance for legal tasks: optimize harnesses, design grading/reward systems, study agent behavior, and collaborate with internal and external researchers to convert findings into training data, evals, and model improvements.

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

Design and optimize scalable geospatial data pipelines to convert high-frequency vehicle sensor telemetry into high-fidelity map features. Build map-matching, trajectory smoothing, and sensor-fusion algorithms, apply statistical tests for change detection and map validation, and lead system architecture for low-latency spatial indexing and storage while collaborating with embedded, perception, and cloud teams.

The AI Engineer will design, develop, and deploy machine learning models, work on APIs for AI applications, and manage ML pipelines on AWS. Responsibilities include productionizing AI systems and developing scalable solutions in a cloud-native environment.