Lead Machine Learning Engineer (Safety)

As Lead Machine Learning Engineer at Faculty, you will shape technical strategy and scalable ML infrastructure for high-stakes AI systems. You’ll bridge research and production, driving safe, practical AI solutions across cross‑functional teams and client contexts. You’ll mentor engineers, set standards for responsible deployment, and collaborate with Frontier Labs to advance governance and safety. This role offers impact by turning cutting‑edge AI into reliable, enterprise‑grade software that helps clients achieve real outcomes.

• Steer technical strategy, system architecture, and rigorous testing for complex ML systems and Frontier AI models
• Architect production-grade, secure ML software, orchestration tools, and scalable cloud infrastructure
• Establish company-wide standards for safe, reliable, and ethical ML deployment at scale
• Collaborate with cross-functional teams and executive stakeholders to translate client needs into robust AI solutions
• Advise senior clients and partners as a trusted AI safety and technical authority
• Mentor and develop engineering talent, fostering a high-performing, inclusive culture
• Partner with Frontier Labs to enhance safety evaluation procedures, multi-agent tooling, and AI governance frameworks

• Leadership experience designing, deploying, and securing scalable LLM applications and multi-agent frameworks
• Exceptional software engineering fundamentals with advanced Python expertise
• Hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and cybersecurity
• mastery of the full ML lifecycle with production systems using PyTorch, TensorFlow, or similar
• Autonomy, strategic ownership, and proven ability to guide technical decisions in fast-growth environments
• Strong communication skills for technical and senior non-technical stakeholders

• leadership
• strategic thinking
• clear communication
• Python
• AWS, Azure, GCP
• Docker