Research Lead

Job description:
Job Description
The Research Lead – AI Security serves as the senior scientific authority responsible for driving core research agendas within the AI Security Technical Innovation Centre. The role focuses on advancing foundational and applied research in AI security, translating emerging ideas into validated prototypes, and ensuring scientific excellence across model forensics, unlearning, and AI supply-chain assurance.
͏ Key Responsibilities
- Lead deep technical research across AI security areas including model behaviour analysis, provenance, and unlearning.
- Define research hypotheses, experimental design, and validation criteria for differentiated AI security techniques.
- Drive development of proofs of concept, benchmarks, and experimental frameworks.
- Act as subject-matter expert for model fingerprinting, anomaly detection, derivation analysis, and unlearning verification.
- Review and guide work of applied researchers and research engineers to maintain scientific rigour.
- Contribute to system architecture discussions in collaboration with the CTO / Chief Scientist.
- Author and co-author technical papers, research reports, and intellectual property.
- Stay current with global research trends and continuously revalidate internal research direction.
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Education:
- PhD (CS, AI/ML, Crypto, Cybersecurity) from Premier Institute** with strong research credentials in AI/ML, Computer Science, Cybersecurity, or related fields.
Experience:
- 8–12+ years of experience in AI/ML research or applied security research.
- Proven experience leading research initiatives or acting as technical lead for complex research programs.
- Earlier Experience: R&D labs, Research projects, Product companies, AI Projects, Enterprise solutions, AI Engineering
- Publications in top tier conferences (mandatory)
͏ Required Technical Skills
- Strong expertise in large language models, fine-tuning, LoRA/adapters, and retrieval-augmented generation.
- Deep understanding of model internals including weight-space analysis, activation patterns, and interpretability.
- Hands-on experience with AI security techniques such as fingerprinting, backdoor detection, model similarity, and provenance analysis.
- Knowledge of model unlearning methods, verification techniques, and privacy assurance trade-offs.
- Proficiency with ML experimentation frameworks (e.g., PyTorch/JAX) and reproducible research workflows.
Personal Attributes
- Strong analytical mindset and scientific curiosity.
- Ability to operate independently on complex, ambiguous research problems.
- Clear technical communication skills and collaborative work style.
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Mandatory Skills: Embedded Security Engineering .
Experience: >10 YEARS .
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Work arrangement
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