• Research, train and ship models end-to-end. • Work across the stack on novel, hard-to-get data. • Run scaling experiments and create rigorous evals. • Tighten the loop between what's deployed and what you train next.

• Implement product features across ASR, NLP domains powered by pre-trained models • Adapt, fine-tune, and orchestrate pre-trained models to specific challenges • Implement retrieval-based pipelines and agentic reasoning flows • Run quantitative and qualitative evaluations to measure performance • Collaborate with backend/frontend engineers to deploy AI features in production

• delivering solutions that help our clients identify payment integrity issues, reduce the cost of healthcare processes, or improve the quality of healthcare outcomes. • conduct independent model validation of existing models for benchmarking, assessment, and gauging effectiveness. • determine aspects of model drift and related data drift for the purpose of model risk management (MRM). • apply deep expertise with AI/ML/GenAI model development, including hands-on experience with model building and model evaluation. • benchmark and potentially rebuild existing models as needed using updated data, and potentially newer, more modern and effective algorithms. • actively drive improvements in model monitoring activities, including methods for model registration, model metadata management, and conceptualizing approaches for related tools and techniques. • complete all responsibilities as outlined in the annual performance review and/or goal setting. • complete all special projects and other duties as assigned.

Role Description 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. Qualifications - 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. Requirements - 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. Benefits - 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.