Top Artificial Intelligence Machine Learning Engineer Jobs

The ERP AI Engineer - Manager at PwC leads teams in AI/ML engineering, manages client relationships, and designs AI solutions, all while mentoring team members and addressing complex business challenges.

Lead the development of production-grade ML systems, aligning technical roadmaps with company priorities, mentoring ML engineers, and ensuring ethical use of ML/AI.

Build and deploy AI-powered solutions (including LLM-based workflows) for healthcare clients. Implement integrations, develop production-ready code, support testing and deployments, assist with data preparation and prompt configuration, troubleshoot issues, and collaborate with cross-functional teams to ensure solutions are scalable, secure, and transitioned to client engineering teams.

As a Machine Learning Engineer, you will develop machine learning models, automate data engineering pipelines, and support customer-centric projects while collaborating with data scientists and engineers.

The Senior Machine Learning Engineer will develop and enhance Moveworks' NLU and agentic AI capabilities, deliver production-quality code, and solve complex engineering challenges in conversational AI.

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, build, and optimize machine learning models and scalable data pipelines while collaborating with cross-functional teams to enhance Micron's products using AI/ML techniques.

This role involves developing AI and machine learning models for vaccine strain selection, integrating biological data, and validating models through rigorous benchmarking while collaborating with various scientific disciplines.

Lead the development of large-scale recommendation systems at Snap, overseeing technical leadership, collaboration across teams, and implementation of machine learning strategies to enhance content discovery and personalization.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.