SentinelOneRemote
Principal AI Software Engineer
United States of America
Full Time
3 weeks ago
$224,000 - $308,000 USD
No H1B
Key skills
PythonAIMachine LearningMLOpsKubeflowAWSGoogle CloudKubernetesDockerCollaboration
About this role
SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. As a Principal AI Software Engineer, you will lead the development and deployment of next-generation, AI-powered cybersecurity solutions, shaping the company's competitive advantage and directly impacting the delivery of industry-leading protection to customers.
Responsibilities:
- Define and execute the technical roadmap for AI engineering, focusing on bringing the most recent advances in machine learning technology to help our customers efficiently discover and defeat threats
- Serve as a lead IC who also mentors across distributed teams, fostering a culture of technical excellence and continuous innovation
- Design and build net-new AI capabilities to be used across SentinelOne’s industry-leading cybersecurity platform
- Drive cross-functional collaboration across multiple engineering teams to ensure seamless integration and innovation in AI-powered cybersecurity products
- Oversee the establishment of best practices for MLOps, security, and performance monitoring to ensure the reliability and efficacy of our AI defense mechanisms
Requirements:
- 15+ years of progressive experience in software or machine learning engineering
- Proven track record of delivering multiple high-scale, production-ready AI or data products from concept through deployment and maintenance
- Deep expertise in building highly available, low-latency machine learning systems, including experience with machine learning workflows and processes
- Experience in endpoint security, OS concepts, or a related field with comparable large-scale event processing and real-time processing challenges
- Demonstrated ability to mentor, develop, and retain top-tier AI engineering talent
- Expert-level knowledge of Python, cloud-native MLOps platforms (e.g., Kubeflow), and large-scale data processing technologies, including hands-on familiarity with Docker, Kubernetes, and operating in cloud environments, especially AWS and Google Cloud