Top Hybrid Machine Learning Engineer Jobs

Lead applied machine learning research to enhance ML validation for robotics and autonomous driving systems, focusing on simulation-based evaluation and prototype development.

Lead the technical roadmap for the Ad Marketplace, design and optimize machine learning models, and collaborate with teams to meet objectives.

The Research Engineer will optimize large-scale machine learning models, contribute to architecture and training, and collaborate with teams for kernel integration.

Join Freed as an ML Engineer to optimize speech recognition models and infrastructure. Contribute to AI solutions improving clinician satisfaction.

The role involves building machine learning models, collaborating with cross-functional teams, and translating complex data insights into business solutions.

Design and build AI-powered consumer products, partner with cross-functional teams, and lead the development of machine learning systems.

The Lead Machine Learning Engineer will design and deploy ML models, optimize AI systems, and collaborate with cross-functional teams to enhance banking solutions.

Lead experimentation, develop predictive models, and guide product strategies to improve user onboarding and retention in therapy applications.

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

The role involves deploying ML models for ads, collaborating with teams to enhance solutions, and mentoring junior scientists, requiring advanced technical and communication skills.

As an AI/ML Engineer Intern, contribute to the design, development, and deployment of AI/ML models, collaborate with teams, and optimize algorithms for business applications.

Analyze malware and detection tickets to improve detection capabilities. Collaborate with internal teams on threat detection inquiries and perform tasks to manage false positives.

As a Machine Learning Platform Engineer, you will partner with teams to establish data science environments, oversee analytic solutions, and lead complex projects while enhancing collaboration between technology and data science teams.