Metacognition - ML Platform / DevOps Engineer
Responsibilities
• Engineering • Engineering • Build and automate development environments that allow teams to safely experiment with AI, data and application development. • Design and implement CI/CD pipelines and deployment patterns, particularly using GitHub and GitHub Actions. • Develop reusable infrastructure templates, provisioning workflows and automation for rapid and consistent environment creation. • Establish environment access controls, governance and operational guardrails across development and production environments. • Build and maintain integrations between GitHub, cloud services and supporting technology platforms. • Establish DevSecOps practices, embedding appropriate security, compliance and operational controls into platform delivery. • Develop standards and reusable patterns that can be adopted across multiple development teams. • Collaboration • Work with engineering and research teams to onboard testing environments; streamline release and provide compute and data scaling for on-going and ad-hoc research projects. • Advise development teams on platform architecture, deployment, scalability, operational support and long-term maintainability. • Improve developer workflows and establish practices that enable teams to move rapidly from experimentation through to production. • Contribute to a rigorous and supportive engineering culture, sharing knowledge and helping establish effective platform engineering practices. • Essential • Strong experience in platform engineering, DevOps. • Strong experience with GitHub and GitHub Actions, including CI/CD and automated deployment. • Hands-on experience with cloud platforms, ideally AWS or GCP. • Experience with Infrastructure as Code and infrastructure automation (i.e. Terraform, Pulumi). • Experience designing and automating development environments, deployment workflows and cloud infrastructure. • Solid understanding of cloud security, governance, and role-based access controls. • Experience with containers and Kubernetes-based environments. • Strong software engineering skills and an understanding of the developer journey from development and experimentation through production. • Preferred • Familiarity with deploying and integrating agentic workflows into supporting platforms to accelerate company knowledge sharing and processes (github, aws, glean, slack, linear) • Experience working with AI/ML platforms at scale (training/serving). • Experience establishing DevSecOps practices across cloud and application environments. • Experience building internal developer platforms, self-service infrastructure or reusable platform capabilities. • Experience designing platforms that support the transition of AI and data prototypes into production services. • Familiarity with modern cloud-native development and observability practices. • __________________________
Benefits
• You’ll work alongside leading researchers, engineers and thinkers to build intelligent systems capable of learning, remembering and reasoning over time. We're an early stage startup with ambitious goals. Joining us will give you a genuine opportunity to shape both our technology and the company we become. • What you can expect • What you can expect • Competitive remuneration • The opportunity to work with leading researchers, engineers and industry experts • A highly collaborative, fast-moving environment
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