10 hours ago
London, United KingdomStaff+
Responsibilities
- Drive technical strategy, system architecture, and rigorous capability testing for complex ML systems and frontier AI models.
- Architect production-grade secure ML software, orchestration tools, and scalable cloud infrastructure.
- Establish standards and best practices for safe, reliable, and ethical machine learning deployment.
- Translate client needs into resilient AI solutions while advising senior clients and partners on AI safety and technical strategy.
- Develop safety evaluation procedures, multi-agent harness tooling, and AI governance frameworks with Frontier Labs.
- Mentor engineering talent and guide cross-functional technical decisions.
Requirements
- Significant leadership experience designing, deploying, and securing scalable LLM applications, multi-agent frameworks, and AI safety evaluation processes.
- Exceptional software engineering fundamentals and advanced Python expertise.
- Deep hands-on experience with AWS, Azure, or GCP; scalable cloud architectures; Docker; Kubernetes; agent sandboxing; and end-to-end cybersecurity.
- Mastery of the full ML lifecycle and proven experience operationalizing production systems using PyTorch, TensorFlow, or similar frameworks.
- Strategic ownership, autonomy, and experience guiding technical decisions in fast-paced, high-growth environments.
- Strong communication skills with technical teams and senior non-technical stakeholders.
Benefits
- The company supports conversations about part-time hours.
- The interview process includes a talent team screen, hiring manager introduction, pair programming interview, system design interview, and commercial and leadership interview.
- Candidates may opt out of the AI note-taker used during interviews by notifying the company.
Tech Stack
Categories
AI ResearchML Engineering
