AI Researcher

Vega is one of the fastest-growing startups in cybersecurity, redefining security analytics and operations with an AI-native platform for the SOC. We are building the next-generation operating system for security teams. Vega is already delivering real impact at some of the world’s largest organizations - improving detection, unlocking the value of their security data, and reducing cost and complexity. With HQs in New York and TLV, we're looking for people who want to be a part of the next rocket-ship in cyber.

As an AI Researcher, you’ll be part of the team building the AI systems that power our products. You’ll work across research and engineering - from training and post-training state-of-the-art LLMs to applying them to challenging real-world problems such as agentic behavior, coding, and security-related decision-making.

This is a hands-on research role with a strong focus on taking ideas from experimentation to production. You’ll have significant ownership and the opportunity to shape both the direction of our AI research and the systems we build around it.

what You Will Do

• Train, adapt, and scale state-of-the-art LLMs using techniques such as post-training SFT, knowledge distillation, LoRA, and reinforcement learning.
• Develop and evaluate new algorithms and approaches, bridging cutting-edge academic research with production-grade AI systems.
• Optimize models for production, balancing quality, latency, throughput, and cost at scale.
• Own the AI lifecycle—from algorithmic prototyping and synthetic data generation to evaluation, deployment, and iteration of production AI features.
• Work closely with engineering and product teams to translate research advances into impactful AI capabilities.
• Lead research initiatives that advance the capabilities of Vega’s AI systems.
  
  

• 5+ years of professional experience as a research scientist, applied scientist, or algorithms/ML engineer.
• Ph.D. or Master’s degree in computer science, machine learning, data science, statistics, or a related field.
• Expert-level proficiency in PyTorch and deep learning training and inference optimization.
• Hands-on experience training, fine-tuning, evaluating, or optimizing large-scale LLMs in research or production environments.
• Strong Python programming skills and experience writing production-grade ML code.
• Experience working with large-scale data and ML infrastructure.
  
  

• Experience applying AI/ML to cybersecurity, including threat detection, incident response, log analysis, or related problems.
• Experience with reinforcement learning or preference optimization for LLMs.
• A track record of research publications, open-source contributions, or other technical work demonstrating research depth.