Dream is a pioneering AI cybersecurity company delivering revolutionary defense through artificial intelligence. Our proprietary AI platform creates a unified security system safeguarding assets against existing and emerging generative cyber threats. Dream's advanced AI automates discovery, calculates risks, performs real-time threat detection, and plans an automated response. With a core focus on the "unknowns," our AI transforms data into clear threat narratives and actionable defense strategies.

Dream's AI cybersecurity platform represents a paradigm shift in cyber defense, employing a novel, multi-layered approach across all organizational networks in real-time. At the core of our solution is Dream's proprietary Cyber Language Model, a groundbreaking innovation that provides real-time, contextualized intelligence for comprehensive, actionable insights into any cyber-related query or threat scenario.

We’re seeking an experienced AI/ML Engineer to join our AI Infra Group, the group behind Dream’s next-generation GenAI platform. This group builds scalable, high-performance AI systems for internal users and external customers, designed to run seamlessly across cloud and on-premise environments using the latest hardware advancements.

In this role, you’ll lead efforts in distributed training, large-scale inference, resource optimization, and end-to-end model lifecycle management following MLOps best practices. Your work will accelerate research, strengthen production-grade AI infrastructure, and advance Dream’s internal AI ecosystem.

• Architect and build scalable ML infrastructure for training and inference workloads across heterogeneous compute environments (on-premise and cloud)
• Design and implement distributed systems to support model lifecycle management - from data ingestion and preprocessing, to training orchestration and deployment
• Optimize performance and cost-efficiency of large-scale model training and serving pipelines using technologies like Ray, Kubernetes, Spark, and GPU schedulers
• Collaborate with AI researchers, data scientists, and product teams to understand their workflows and translate them into reusable platform services and APIs
• Drive adoption of best practices for CI/CD, observability, and reproducibility in ML systems
• Contribute to the long-term vision and technical roadmap of the ML platform, ensuring it evolves to meet the growing demands of AI across the company

• 5+ years of experience building large-scale distributed systems or platforms, preferably in ML or data-intensive environments
• Proficiency in Python with strong software engineering practices, familiarity with data structures and design patterns
• Deep understanding of orchestration systems (e.g., Kubernetes, Argo) and distributed computing frameworks (e.g., Ray, Spark)
• Experience with GPU compute infrastructure, containerization (Docker), and cloud-native architectures
• Proven track record of delivering production-grade infrastructure or developer platforms
• Solid grasp of ML workflows, including model training, evaluation, and inference pipelines