AI Research Scientist
AI Research Scientist
Location: Bengaluru/Ireland
About Disseqt The assurance layer for enterprise AI governance. Test before deployment, protect at runtime, monitor after go-live — covering jailbreaks, bias, hallucination, policy violations, and regulatory compliance. Customers are GSIs, financial institutions, and large enterprises navigating EU AI Act, NIST AI RMF, ISO 42001.
The role in one line - You advance the frontier of what's detectable and measurable in AI systems, then hand that work directly to engineers to productise. Research that ships, not research that sits.
What you own
Research and IP development — expand the jailbreak and adversarial attack library beyond public methods; build bias, fairness, and hallucination eval methodologies that hold up to regulators; develop interpretability techniques that explain why a model failed (not just that it failed); maintain a live radar on AI safety literature.
Productisation bridge — translate findings into engineering specs the red team and MLOps engineers can build from without hand-holding; define ground truth for eval harnesses; own the methodology documentation behind customer compliance reports.
External credibility — publish papers, technical posts, benchmark releases; represent Disseqt at conferences and regulator briefings; contribute to open benchmarks where it builds community trust.
Must-haves Strong ML foundations at architecture level, not just API level. Hands-on evaluation, bias, or AI safety experience. Code that runs in production, not just notebooks. Clear technical writing for two audiences (compliance officer and research peer). Genuine interest in AI governance as a problem space.
Strong signals Published work in AI safety, fairness, interpretability, or adversarial ML. LLM red teaming experience. Familiarity with EU AI Act, NIST AI RMF, ISO 42001. Prior startup experience where research had to ship.
Probably not for you if You want to work on benchmark maximisation for foundation model capabilities. You need a large team around you. You're uncomfortable with your research being used commercially and scrutinised by auditors.
Stack Python, PyTorch, HuggingFace, open-weight and API-accessed frontier models. Fluent enough in the engineering environment to hand off cleanly — not a full-stack role, but you don't stop at a notebook.
What's on offer Protected research time that doesn't get eaten by sprints. Publishing support — your research output belongs to you as much as Disseqt. Direct access to live enterprise AI risk problems, not synthetic toy datasets. Competitive salary, meaningful equity, early-stage upside.
Location: Bengaluru/Ireland
About Disseqt The assurance layer for enterprise AI governance. Test before deployment, protect at runtime, monitor after go-live — covering jailbreaks, bias, hallucination, policy violations, and regulatory compliance. Customers are GSIs, financial institutions, and large enterprises navigating EU AI Act, NIST AI RMF, ISO 42001.
The role in one line - You advance the frontier of what's detectable and measurable in AI systems, then hand that work directly to engineers to productise. Research that ships, not research that sits.
What you own
Research and IP development — expand the jailbreak and adversarial attack library beyond public methods; build bias, fairness, and hallucination eval methodologies that hold up to regulators; develop interpretability techniques that explain why a model failed (not just that it failed); maintain a live radar on AI safety literature.
Productisation bridge — translate findings into engineering specs the red team and MLOps engineers can build from without hand-holding; define ground truth for eval harnesses; own the methodology documentation behind customer compliance reports.
External credibility — publish papers, technical posts, benchmark releases; represent Disseqt at conferences and regulator briefings; contribute to open benchmarks where it builds community trust.
Must-haves Strong ML foundations at architecture level, not just API level. Hands-on evaluation, bias, or AI safety experience. Code that runs in production, not just notebooks. Clear technical writing for two audiences (compliance officer and research peer). Genuine interest in AI governance as a problem space.
Strong signals Published work in AI safety, fairness, interpretability, or adversarial ML. LLM red teaming experience. Familiarity with EU AI Act, NIST AI RMF, ISO 42001. Prior startup experience where research had to ship.
Probably not for you if You want to work on benchmark maximisation for foundation model capabilities. You need a large team around you. You're uncomfortable with your research being used commercially and scrutinised by auditors.
Stack Python, PyTorch, HuggingFace, open-weight and API-accessed frontier models. Fluent enough in the engineering environment to hand off cleanly — not a full-stack role, but you don't stop at a notebook.
What's on offer Protected research time that doesn't get eaten by sprints. Publishing support — your research output belongs to you as much as Disseqt. Direct access to live enterprise AI risk problems, not synthetic toy datasets. Competitive salary, meaningful equity, early-stage upside.