Description
As a Lead Software Engineer at Faculty, you'll drive the technical strategy, architecture, and deployment of cutting-edge, secure AI systems across high-stakes domains.
Bridging AI research and production impact, you'll 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, articulating complex strategies clearly
- Mentoring and developing engineering talent, actively elevating team capabilities 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 building 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
- Experience in fast-paced, high-growth environments, bringing autonomy, strategic ownership, and a track record of guiding technical decisions
- Ability to communicate persuasively with both technical teams and senior non-technical stakeholders, translating complex technical risks into clear strategic direction