ML Engineering Team Lead - Applied AI Engineering Group

At Dream, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; it’s a Dream job. Dream is where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Let’s build something extraordinary together.

Dream's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Central to our Dream's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.

At Dream, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.

The Dream Job:

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards

If you want to grow your skills building AI products for mission-critical AI, join Dream’s mission - this role is for you.

The Dream-Maker Responsibilities:

• Lead and mentor ML Engineers delivering production ML systems.
• Own technical direction for model architectures, training pipelines, and evals.
• Review designs and code; ensure scalability, reliability, and data quality.
• Define standards for experimentation, reproducibility, and monitoring.
• Partner with product and platform teams on roadmap and prioritization.
• Balance hands-on technical contribution with people management.
• Build a culture of measurement, rigor, and continuous improvement.
  
  

• 6+ years software engineering experience with 4+ years applied ML.
• Prior experience as a technical lead or engineering manager.
• Deep understanding of ML training, evaluation, and deployment lifecycles.
• Strong experience with ML frameworks and large-scale data pipelines.
• Proven ownership of ML evals, monitoring, and drift detection in production.
• Experience with distributed training and performance optimization.
• Strong experience with feature stores, model registries, and experiment tracking.
• Experience deploying and operating ML systems on Kubernetes or similar platforms.
• Strong Python background and system architecture expertise.
• Experience managing cross-functional ML initiatives and stakeholders.