The Role You will build the science behind our platform
- the attacks that break AI systems, and the models that stop them. This is genuine research with a short path to production. Your work ships into live enterprise deployments, gets scrutinised by auditors and regulators, and can be published. What you'll work on
• Adversarial research
- designing jailbreak agents, multi-turn attacks, and measuring how attacks transfer across models
• Validator models
- training, fine-tuning, and calibrating the models that catch unsafe AI behaviour in real time
• Evaluation methodology
- building the statistics behind claims like “this model is secure”
• Interpretability & fairness
- explaining why a model failed, in a form an auditor accepts
• New attack surfaces
- multi-modal, RAG, and agentic tool-calling, where almost no methodology exists yet What We're Looking For
• Transformers
- you understand attention, tokenisation, and model internals,



not just the API
• Classical ML
- supervised learning, ensembles, calibration, precision/recall trade-offs, imbalanced data. This matters as much as the LLM work.
• Fine-tuning
- SFT, LoRA/PEFT, distillation. Awareness of RLHF, reward modelling, and DPO.
• Statistics
- experimental design, significance, confidence intervals
• Python
- clean, reproducible code. PyTorch, HuggingFace, scikit-learn. Who You Are
• Curiosity about how systems break
• Comfort with problems that have no textbook answers
• Clear writing
- you can explain your work to a researcher and to a compliance officer Bonus points
• Publications or preprints
• Red-teaming or CTF experience
• Open-source contributions
• Security background

📌 Sr. AI Research Scientist (Bengaluru)
🏢 Disseqt AI
📍 Bengaluru