Lead Machine Learning Engineer

As a Lead Machine Learning Engineer at Faculty, you will set the technical direction for complex AI/ML projects, ensuring models perform at scale and in production over time by balancing technical trade-offs and guiding team priorities.

You will lead the delivery of large-scale AI-powered platforms in high-risk environments while defining project roadmaps across multiple complex workstreams.

This is an ambitious, entrepreneurial leadership role where you will act as a trusted technical expert, defending your architectural rationale to senior stakeholders to ensure we deliver high-quality, high-value outputs.

Responsibilities:

• Designing, implementing, and maintaining reliable, scalable ML systems while justifying key architectural decisions for production environments.
• Driving the development of shared libraries and infrastructure for model deployment, lifecycle management, and CI/CD pipelines.
• Leading model integration with infrastructure by creating APIs and services that enable scalable AI functionality in applications.
• Overseeing the delivery of multiple complex workstreams and defining project problems in high-risk environments.
• Ensuring reliable model performance by defining testing frameworks and model versioning systems for senior engineers to implement.
• Managing and coaching multiple individuals, setting team-wide development goals to improve technical depth and client delivery.
• Executing proactive recommendations for adopting new technologies and AI frameworks to maintain Faculty's competitive market position.

Requirements:

• You are an expert at defining technical roadmaps and managing project priorities to deliver high-stakes outcomes within high-growth environments.
• You possess mastery of cloud-native ecosystems and orchestration tools like Kubernetes to automate complex model lifecycles and robust CI/CD pipelines.
• You have a proven ability to design large-scale, AI-powered platforms and provide the technical justification for critical architectural decisions in high-risk environments.
• You bring expert-level experience in operationalising models within frameworks like TensorFlow or PyTorch to solve complex, high-impact business challenges.
• You define the engineering standards and architecture patterns for agentic systems, ensuring teams build to a consistent, production-grade bar.
• You demonstrate an exceptional ability to align multi-disciplinary technical teams with broader business objectives and evolving customer needs.

Benefits: We offer a range of benefits, including the opportunity to work on impactful projects, a collaborative and dynamic work environment, and a commitment to diversity and inclusion.

Interview Process:

1. Talent Team Screen (30 minutes)
2. Introduction to the Hiring Manager (30 minutes)
3. Pair Programming Interview (90 minutes)
4. System Design Interview (90 minutes)
5. Commercial & Leadership Interview (60 minutes)