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About the Role
Join Faculty as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for diverse clients. You will bridge the gap between AI research and real-world impact by building scalable, production-grade machine learning systems. Partnering with clients and cross-functional teams, you will champion technical feasibility, ensure seamless delivery, and collaborate with Frontier Labs to reinforce our leadership in practical, high-stakes AI safety.
Responsibilities
- Building and deploying production-grade ML software, tools, and infrastructure.
- Creating reusable, scalable solutions that accelerate the delivery of ML systems and capability testing of Frontier AI models.
- Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges.
- Leading technical scoping and architectural decisions to ensure project feasibility and impact.
- Defining and implementing Faculty’s standards for deploying machine learning at scale.
- Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.
Requirements
- Comfortable building LLM applications, and familiar with multi-agent harness tooling and AI Safety evaluation procedures.
- Strong Python skills and solid experience in software engineering best practices.
- Hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
- Experience with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale.
- Comfortable with core ML concepts, including probability, statistics, and common learning techniques.
- Understanding of the full machine learning lifecycle and experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
- Excellent communication skills, able to guide technical teams and confidently advise non-technical stakeholders.
- Ability to thrive in a fast-paced environment, with autonomy to own scope, solve and deliver solutions.
Skills
- Python
- Software Engineering
- Cloud Platforms (AWS, Azure, GCP)
- Containerization (Docker)
- Orchestration (Kubernetes)
- Machine Learning Concepts
- Machine Learning Lifecycle
- Scikit-learn
- TensorFlow
- PyTorch
- LLM Applications
- Multi-agent Harness Tooling
- AI Safety Evaluation
Work Type
- Full-time
About the Company
- Faculty was established in 2014 with the belief that AI would be the most important technology of our time.
- Faculty has worked with over 350 global customers to transform their performance through human-centric AI.
- Faculty innovates, builds, and deploys responsible AI that makes a significant impact.
- Faculty possesses unparalleled depth of technical, product, and delivery expertise.
- Faculty serves clients across government, finance, retail, energy, life sciences, and defense sectors.
- Faculty's business and reputation are growing rapidly.
- Faculty seeks individuals with intellectual curiosity and a desire to build a positive legacy through technology.
- Faculty empowers employees to envision and implement the most powerful applications of AI.
- The National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability.
- This unit strengthens mission-critical capabilities across national security and intelligence.
- This unit works with frontier labs to provide robust AI safety red teaming and evaluation.
- This unit operates at the frontier of high-stakes, high-impact missions.
- Faculty partners with Government and major tech organizations to ensure AI is secure, trustworthy, and safe.
Equal Opportunity
- Faculty aims to grow the best team, not the most similar one, fostering diversity of thought and strengthening the principle of seeking truth.
- Diverse teams deliver better work, relevant to the world.
- The team is united by deep intellectual curiosity and a desire to use abilities for measurable positive impact.
- Applications are strongly encouraged from people of all backgrounds, ethnicities, genders, religions, and sexual orientations.
- Candidates who do not meet all requirements but are excited by the role and bring key strengths are encouraged to apply.
- Conversations about part-time hours are welcome.
- AI may be used for research and interview prep, but not for generating answers during live interviews.
- Faculty uses an AI note-taker (Metaview) in interviews, with an option to opt-out.
- Every application is reviewed by a human, never decided by AI.