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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning software and infrastructure, with strong proficiency in Python and cloud platforms like AWS, Azure, or GCP. Capable of leading architectural decisions and translating complex ML concepts for diverse stakeholders.
Highest-signal resume keywords
Machine Learning Software DevelopmentPython ProgrammingCloud Infrastructure (AWS, Azure, GCP)Docker and KubernetesMachine Learning Lifecycle Understanding
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningPythonCloud PlatformsDockerKubernetesScikit-learnTensorFlowPyTorchSoftware Engineering Best PracticesAI Safety Evaluation
Soft Skills
Excellent CommunicationProblem SolvingCollaborationAdaptabilityTechnical Advising
Industry Keywords
LLM ApplicationsMulti-Agent Harness ToolingProbabilityStatisticsLearning TechniquesModel OperationalizationArchitectural DecisionsProject FeasibilityStakeholder EngagementUK Work Authorization
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnTensorflow
About the role
Key responsibilities & impact- Build and deploy production-grade machine learning software, tools, and infrastructure
- Create reusable, scalable solutions that accelerate delivery of ML systems and capability testing of Frontier AI models
- Collaborate with engineers, data scientists, and commercial leads to solve critical client challenges
- Lead technical scoping and architectural decisions to ensure project feasibility and impact
- Define and implement Faculty’s standards for deploying machine learning at scale
- Act as a technical advisor to customers and partners, translating complex ML concepts for stakeholders
- Participate in Talent Team Screen, Pair Programming Interview, System Design Interview, and Commercial Interview stages
Requirements
What you’ll need- Comfortable building LLM applications
- Familiarity with multi-agent harness tooling and AI Safety evaluation procedures
- Strong Python skills
- Solid experience with software engineering best practices
- Hands-on experience with cloud platforms and infrastructure such as AWS, Azure, or GCP, including architecture and security
- Experience with Docker and Kubernetes for building and managing applications at scale
- Comfortable with core machine learning concepts, including probability, statistics, and common learning techniques
- Understanding of the full machine learning lifecycle
- Experience operationalising models built with Scikit-learn, TensorFlow, or PyTorch
- Excellent communication skills for guiding technical teams and advising non-technical stakeholders
- Ability to thrive in a fast-paced environment and independently own scope, solve problems, and deliver solutions
- Must address UK work authorization/visa sponsorship requirements
Benefits
Comp & perks- The company is open to conversations about part-time hours
- Interview AI note-taker (Metaview), with the option to opt out
- Diversity and inclusion commitment across backgrounds, ethnicities, genders, religions, and sexual orientations