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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing cloud-native ML infrastructure on Kubernetes, with a strong focus on automation, resource optimization, and collaboration with research teams. Proficient in managing CI/CD pipelines and ensuring high availability of ML platforms.
Highest-signal resume keywords
Python ProgrammingKubernetes ManagementML Infrastructure DevelopmentInfrastructure as CodeCI/CD Pipeline Implementation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Go ProgrammingML Workloads on KubernetesTerraformHelmKapitanGitOps WorkflowsJAXPyTorchGPU Infrastructure ManagementResource Optimization
Soft Skills
Autonomous WorkBest-Practice Guidance
Tools & Technologies
AWSGCPFlytePantsHPC EnvironmentsSlurmPBSTritonTorchServeKServe
Industry Keywords
ML InfrastructureDistributed ML TrainingExperiment Tracking SystemsModel RegistriesCloud Spending OptimizationMulti-Environment ConfigurationsScientific Computing WorkflowsData ManagementMulti-Tenant ML PlatformsResearch Team Support
Tech Stack
Tools & technologiesAWSCloudGoogle Cloud PlatformKubernetesOpen SourcePythonPyTorchTerraformGo
About the role
Key responsibilities & impact- Design and implement a cloud-native ML infrastructure platform on Kubernetes
- Enable researchers to train, evaluate, and deploy models without infrastructure complexity
- Implement and maintain CI/CD pipelines, model registries, experiment tracking systems, and deployment automation
- Build infrastructure for distributed ML training across many GPUs
- Implement data pipelines, checkpointing, and resource optimisation
- Ensure 99.9% uptime through monitoring, alerting, and automated recovery systems
- Manage complex multi-environment Kubernetes configurations with Kapitan
- Ensure consistent deployments across development, staging, and production
- Optimise cloud spending and computational throughput through resource management
- Create tools and abstractions that improve researcher developer experience
- Collaborate with ML Researchers, Chemists, Materials Scientists, and Software Engineers
- Help shape CuspAI’s ML infrastructure strategy and platform architecture
- Work with the VP ML Engineering in a newly created team
Requirements
What you’ll need- Strong Python and/or Go programming skills for infrastructure automation
- Deep expertise in building and maintaining ML infrastructure
- Deep experience with ML workloads on Kubernetes and multi-cloud platforms, including AWS, GCP, and neoclouds
- Expert-level proficiency in Infrastructure as Code using Terraform, Helm, Kapitan, and GitOps workflows
- Experience operating distributed ML platforms for JAX and/or PyTorch
- GPU infrastructure experience, including managing GPU clusters and performance optimisation
- Previous experience as a Software Engineer before moving into DevOps, MLOps, or ML Infrastructure, or regular coding experience
- Ability to work autonomously as a subject matter expert and provide best-practice guidance
- Personal interest in enabling scientists and the potential applications of CuspAI’s technology
- Experience with the latest relevant technologies, tools, and open source projects
- Bonus: Experience with Flyte, Kapitan, Pants, or similar tools
- Bonus: Experience with HPC environments and job schedulers such as Slurm or PBS
- Bonus: Knowledge of ML serving infrastructure such as Triton, TorchServe, or KServe
- Bonus: Familiarity with scientific computing workflows and data management
- Bonus: Experience with multi-tenant ML platforms
- Bonus: Background supporting research teams
- Existing right to work or visa eligibility for the selected work location
Benefits
Comp & perks- Equity in CuspAI
- 28 days holiday in Germany, the Netherlands, and the UK, or 21 days holiday in Japan, Singapore, and the US, in addition to local public holidays
- 26 weeks of parental leave for the primary caregiver at full pay
- 12 weeks of parental leave for the secondary caregiver at full pay
- Professional development budget
- Opportunity to work on meaningful materials science, sustainability, and climate-related problems
- Interdisciplinary teamwork with world-class researchers and engineers
- Reasonable accommodations during or after the interview process