Member of Technical Staff, Trust & Safety Engineer

Accountabilities

• Act as a core Trust & Safety engineering partner embedded within product and research teams, supporting safe design and launch of AI systems from early development through production monitoring.


• Design, build, and maintain safety infrastructure that ensures responsible deployment of generative AI models at large scale.


• Develop and continuously improve red-teaming systems to identify harmful outputs, policy violations, and adversarial behavior before production release.


• Translate ambiguous, evolving trust & safety requirements into concrete, scalable technical solutions and enforcement mechanisms.


• Build internal tooling and systems for content moderation, policy enforcement, abuse detection, and safety evaluation.


• Collaborate with legal, policy, and product teams to define safety rules, interpret guidelines, and implement technical controls.


• Work closely with machine learning researchers to evaluate model behavior and improve safety performance across iterations.


• Contribute to system reliability, performance optimization, logging, monitoring, and incident response for safety-critical infrastructure.


• Support the development of data pipelines and analytical systems to detect abuse patterns and policy violations at scale.


• Continuously improve engineering quality, robustness, and maintainability across safety-related codebases and systems.

Requirements

• 3+ years of software engineering experience in production environments, with strong proficiency in Python and/or TypeScript.


• Experience building and maintaining backend systems, infrastructure, or distributed systems in cloud environments (AWS or GCP).


• Strong ownership mindset with the ability to design, build, and operate systems end-to-end, including monitoring and incident response.


• Experience working across the stack, including backend services, internal tooling, data pipelines, and infrastructure debugging.


• Familiarity with analytics systems or large-scale data infrastructure, ideally involving event data, abuse detection, or behavioral signals.


• Ability to translate ambiguous policy, safety, or compliance requirements into clear technical implementations.


• Strong collaboration and communication skills, especially when working with legal, policy, research, and product stakeholders.


• Experience designing or supporting evaluation systems, red-teaming frameworks, or model safety testing is a plus.


• Comfort working in high-ambiguity environments where processes and solutions are still being defined.


• Strong written communication skills with the ability to document technical decisions and trade-offs clearly.


• Open-minded, proactive, and highly collaborative approach to engineering work.


• Interest in AI safety, generative models, or responsible AI deployment is strongly preferred.

Benefits

• Competitive compensation package ranging from $240K – $290K (based on experience and location adjustments).


• Opportunity to work at the forefront of generative AI and world-model simulation technology.


• Remote-first flexibility across Europe and the US, with optional access to major tech hubs.


• High-impact role shaping safety systems used by millions of users globally.


• Collaborative environment working closely with leading researchers, engineers, and policy experts.


• Strong ownership culture with autonomy to define systems and safety approaches from the ground up.


• Exposure to cutting-edge AI infrastructure, including LLM-based safety systems and large-scale simulation models.


• Inclusive and mission-driven engineering culture focused on responsible AI development.


• Opportunity to work on foundational problems in AI safety, evaluation, and system reliability.

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