We are looking for a Data Scientist to develop and improve Generative AI models for our product, including security data understanding, reasoning, and incident summarization.
You will focus on model development, training data, fine-tuning, and evaluation, working with
AI Engineers and security specialists to turn research and experiments into practical product capabilities.
Key Responsibilities:
Research, develop, and adapt GenAI models for cybersecurity use cases.
Build and curate training datasets from security data, incident reports, and expert annotations, ensuring quality and appropriate train-test separation.
Experiment with model architectures, training approaches, and fine-tuning techniques to improve domain-specific performance.
Explore synthetic data generation and domain adaptation to address limited or specialized training data.
Build evaluation frameworks to measure factual correctness, reasoning quality, evidence grounding, and task completion.
Analyze model failures and improve data, training configurations, and model behavior through reproducible experiments.
Explore model optimization techniques to balance quality, latency, and computational cost.
Partner with AI Engineers to bring validated models into production and incorporate feedback into further development
Required Qualifications:
4-5 years of experience in data science, applied ML, or AI research, including hands-on work developing or fine-tuning GenAI models.
Strong Python skills and experience with PyTorch, Hugging Face, or similar tools.
Solid understanding of deep learning, transformer architectures, and generative modeling fundamentals.
Practical experience preparing training data and building reproducible training and evaluation workflows.
Familiarity with GPU-based experimentation and techniques for efficient training.
Ability to design experiments, diagnose model issues, and explain results and limitations clearly.
A strong passion for cybersecurity and curiosity about applying GenAI to security operations.
Nice to have :
Previous experience applying AI in the cybersecurity domain.
Experience with instruction tuning, LoRA/PEFT, preference optimization, or domain-specific continued pretraining.
Experience with synthetic data generation, model distillation, or quantization.
Experience with multimodal models or training models for tool use and multi-step tasks.
Experience taking research prototypes into production with engineering teams.