Company
Technology
MemberofTechnicalStaff,Trust&SafetyEngineer
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“Member of Technical Staff, Trust & Safety Engineer. Skills: Trust & Safety, generative AI, Python, TypeScript, backend systems. 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 system”
What You'll Achieve.
High-impact role shaping safety systems used by millions of users globally. Opportunity to work on foundational problems in AI safety, evaluation, and system reliability.
Industry & Context.
system design; distributed systems; debugging; technical solutions
Remote-first flexibility across Europe and the US, with optional access to major tech hubs.
What They're Looking For.
Must Have
3+ years of software engineering experience in production environments, with proficiency in Python and/or TypeScript. Experience building and maintaining backend systems, infrastructure, or distributed systems in cloud environments (AWS or GCP). 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. collaboration and communication skills, especially when working with legal, policy, research, and product stakeholders. Comfort working in high-ambiguity environments where processes and solutions are still being defined. written communication skills with the ability to document technical decisions and trade-offs clearly. Open-minded, proactive, and highly collaborative approach to engineering work.
Nice to Have
Experience designing or supporting evaluation systems, red-teaming frameworks, or model safety testing is a plus. Interest in AI safety, generative models, or responsible AI deployment is strongly preferred.
What You'll Do.
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.
How You'll Work.
Team & Collaboration
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. Collaboration and communication skills, especially when working with legal, policy, research, and product stakeholders. Open-minded, proactive, and highly collaborative approach to engineering work.
Communication Scope
collaboration; communication; written communication
Full Job Description
## 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 eve
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