Senior Machine Learning Engineer (Safety)

Overview

As a Senior Machine Learning Engineer at Faculty, you will lead the development and deployment of scalable, production-grade ML systems for high-stakes clients. You’ll bridge AI research and real-world impact, shaping technical strategy and mentoring teams. You’ll work closely with clients and cross-functional colleagues to deliver secure, ethical AI solutions. This role centers on responsible, impactful AI at scale within the National Security & Safety unit.

Responsibilities

• Lead scoping and architecture decisions for high-impact ML systems and model testing
• Design and deploy production-grade ML software, tools, and scalable infrastructure
• Define and implement best practices for deploying ML at scale
• Collaborate with engineers, data scientists, product managers, and commercial teams on client challenges
• Act as a trusted technical advisor translating complex concepts into actionable plans
• Mentor junior engineers and shape engineering culture and depth
• Partner with Frontier Labs to reinforce leadership in AI safety

Key requirements

• Extensive experience building and deploying secure, scalable LLM applications
• Hands-on experience with AI safety evaluation and multi-agent tooling
• Proficiency with ML lifecycle and frameworks like TensorFlow or PyTorch
• Strong software engineering fundamentals and Python expertise
• Cloud platform experience (AWS, Azure, GCP) including architecture and security practices
• Experience with containers and orchestration (Docker, Kubernetes)
• Ability to operate in fast-paced, high-growth environments with ownership and autonomy
• Excellent communication skills for guiding technical and non-technical stakeholders

• Strong communication
• Mentorship and leadership
• Ownership and initiative
• LLM development and deployment
• TensorFlow or PyTorch
• Python programming

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