Top Remote AI Engineer Jobs

As a Senior Generative AI Engineer, you'll develop AI-powered solutions across sales, customer success, and finance, leveraging advanced AI techniques to enhance workflows and drive innovation in ad tech.

As a Staff Software Engineer, you will enhance developer productivity by establishing AI-driven tools, improving processes, and mentoring teams on AI integration and best practices.

The Applied AI Engineer will develop systems and tools for AI integration, designing backend architecture and APIs, and collaborating across teams to implement AI solutions.

AI Engineers at Hiya develop and integrate AI solutions, conduct model evaluations, and optimize large language models, collaborating with cross-functional teams.

Collaborate to design, develop, and deploy AI solutions focusing on Generative AI and autonomous systems, requiring a strong ML engineering foundation.

Lead the design and implementation of simulation-based validation strategies for autonomous systems, mentoring engineers and ensuring performance metrics are met.

As a Conversational AI Developer, you'll design and test AI tools for contact centers and integrate AI functionalities, collaborating in an Agile environment.

The Senior Software Engineer will own quarterly goals, lead the team through challenges, collaborate on product development, and ensure code quality, while also fostering team growth.

The Senior Machine Learning Engineer will develop AI workflows, collaborate with teams on models and pipelines, conduct research, mentor junior members, and innovate solutions for healthcare.

Lead and manage a team of machine learning engineers to develop AI-driven tools for clinicians, ensuring project success and team growth.

As a Staff AI Engineer, you'll design and implement AI systems, develop APIs, and mentor engineers, collaborating with teams for impactful automation in advertising.

As a Cloud Software Engineer at CrowdStrike, you will develop scalable cloud solutions, innovate with Large Language Models, and maintain data pipelines while ensuring high coding quality and collaborating with teams.

As a Backend Engineer, develop and enhance cloud microservices, implement solutions for cyber threats, and work with Large Language Models while collaborating across teams.

Lead the LLM as a Service team to innovate customer experiences, manage scalable AI infrastructure with Kubernetes, and collaborate with security teams to comply with standards.

As a Lead AI/DevOps Engineer, you will manage AI infrastructure, develop solutions for AI use cases, and collaborate with teams to optimize resources for AI workloads.

Lead DevOps initiatives and design AI infrastructure using Kubernetes. Innovate AI solutions, collaborate with security teams, and support customer needs throughout the AI product lifecycle.

Lead DevOps initiatives for AI infrastructure, manage Kubernetes orchestration, develop AI use cases, partner with security, and engage with customers.

Lead AI/DevOps Engineer focused on managing AI infrastructure, driving customer experience strategies, and ensuring compliance with best practices while collaborating with teams to optimize resources.

The Lead AI/DevOps Engineer will design and manage scalable AI infrastructure, drive DevOps initiatives, and collaborate with teams to deliver innovative AI solutions while ensuring compliance and performance.

Lead and develop Agentic AI solutions, mentor team members, collaborate on AI applications, and tackle complex challenges in cybersecurity.

Lead the development of an AI framework for endpoint security, collaborate with teams, optimize solutions, and ensure model efficiency across various platforms.

Lead architecture and development of security features for AI platform, focusing on cryptographic services, identity management, and cross-team collaboration.

Build and maintain high-quality software for AI-powered solutions, including design, implementation, testing, and mentoring team members.

Lead the design and deployment of AI applications, collaborating with teams to integrate LLM models and ensure performance metrics are met, while fostering continuous improvement.

Lead the architecture and management of AI/ML infrastructure using Kubernetes and Terraform, optimizing hybrid environments for performance and scalability.