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AI Security Research Team Lead
HybridFull-timeSecurity ResearchElectronics ManufacturingEnterprise Software
Lead a multidisciplinary team evaluating and improving the security, privacy, safety, and reliability of AI models and applications. Own the roadmap and delivery of an AI evaluation framework, combining applied research, platform engineering, and adversarial testing. Work onsite three days per week,
Responsibilities
- Lead and develop a multidisciplinary team of engineers and researchers
- Own the AI evaluation framework roadmap, architecture, delivery, and production reliability
- Advance evaluation methods, adversarial testing, and automated red teaming for models and agentic applications
- Translate governance and product requirements into measurable assessments, scoring, and actionable reports
- Improve models through evaluation feedback, including training data generation, fine-tuning, and system prompt improvements
- Integrate evaluation and hardening into product and model development and release workflows
Requirements
- At least 7 years of experience in AI/ML, security research, or software engineering, including at least 2 years leading technical teams
- Bachelor’s or master’s degree in computer science or a related field, or equivalent practical experience
- Hands-on experience with LLMs, model evaluation, and model training or fine-tuning
- Strong Python skills and experience delivering production software or research platforms
- Experience designing experiments, datasets, and metrics to assess model behavior and validate improvements
- Ability to own technical roadmaps, mentor team members, and deliver across research and engineering
- Strong communication skills and ability to translate complex findings into clear priorities
Nice to have
- Experience with adversarial machine learning, AI red teaming, prompt injection, jailbreaks, or agentic security
- Experience with LLM-as-judge, benchmark design, automated adversarial testing, or multimodal evaluation
- Experience with adversarial training, safety fine-tuning, preference optimization, or instruction hardening
- Experience translating AI security, privacy, and safety requirements into technical tests and evidence
- Experience building scalable evaluation services, integrating model pipelines, or testing agentic workflows
Benefits
- Career advancement and personal growth opportunities
- Access to training programs
- Performance-based rewards
- Hybrid work arrangement: three days onsite and two days from home