Lead Machine Learning Engineer (Safety)

As a Lead Machine Learning Engineer at Faculty, you will drive the technical strategy, architecture, and deployment of cutting-edge, secure AI systems across high-stakes domains. Bridging AI research and production impact, you will set the benchmark for engineering excellence and operationalise scalable ML infrastructure. Working alongside Frontier Labs, you will champion practical, safety-first AI frameworks, model capability evaluations, and multi-agent systems. You will act as a principal advisor to client leadership, mentor engineering talent, and steer cross-functional teams to solve critical, real-world AI challenges.

Responsibilities

Steering technical strategy, system architecture, and rigorous capability testing for complex ML systems and Frontier AI models; Architecting production-grade, secure ML software, orchestration tools, and scalable cloud infrastructure; Establishing company-wide standards and best practices for safe, reliable, and ethical machine learning deployment at scale; Collaborating with cross-functional teams and executive stakeholders to translate high-impact client needs into resilient AI solutions; Advising senior clients and partners as a trusted AI safety and technical authority; Mentoring and developing engineering talent and nurturing an inclusive, high-performing culture; Partnering with Frontier Labs to advance safety evaluation procedures, multi-agent harness tooling, and robust AI governance frameworks

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, with a focus on resilient, enterprise-ready systems; Deep hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and end-to-end cybersecurity; Mastery over the full ML lifecycle, with proven expertise operationalising production systems using PyTorch, TensorFlow, or similar frameworks; Excels in fast-paced, high-growth environments, with autonomy, strategic ownership, and a track record of guiding technical decisions; Communicates persuasively with technical teams and senior non-technical stakeholders, translating complex technical risks into clear strategic direction