Senior Forward Deployed Engineer

Faculty is seeking a Senior Forward Deployed Engineer to join their National Security and AI Safety business unit. The successful candidate will lead the development and deployment of cutting-edge AI systems for clients.

As a Senior Machine Learning Engineer, you will design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards. This is an ambitious, cross-functional role requiring a blend of technical expertise, engineering leadership, and confident client-facing skills.

Key responsibilities include:

• Leading technical scoping and architectural decisions for high-impact ML systems
• Designing and building production-grade ML software, tools, and scalable infrastructure
• Defining and implementing best practices and standards for deploying machine learning at scale across the business
• Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities
• Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies
• Mentoring and developing junior engineers, actively shaping the team's engineering culture and technical depth

The ideal candidate will have:

• Significant experience operationalising models built with frameworks like TensorFlow or PyTorch
• Deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems
• Demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure
• Extensive experience working with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale
• Experience thriving in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion
• Exceptional communication skills, confidently guiding both technical teams and senior, non-technical stakeholders

The interview process consists of:

1. Talent Team Screen (30 minutes)
2. Pair Programming Interview (90 minutes)
3. System Design Interview (90 minutes)
4. Commercial Interview (60 minutes)

Faculty aims to grow the best team, not the most similar one, and encourages applications from people of all backgrounds, ethnicities, genders, religions, and sexual orientations.