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Tech Stack
Tools & technologiesKubernetesPythonPyTorch
About the role
Key responsibilities & impact- Design, build, and maintain data pipelines that power AI research and experimentation workflows
- Develop backend services and APIs that give researchers clean, reliable access to data at scale
- Work directly with researchers and AI builders to understand their data needs and translate them into robust engineering solutions
- Improve data infrastructure reliability, observability, and performance
- Debug and resolve data quality issues, identifying root causes and sharing learnings broadly
- Contribute to internal tooling and paved roads that make it easier for the team to work with data consistently
Requirements
What you’ll need- Strong Python expertise with experience building production-grade data pipelines or backend services
- Experience with data engineering patterns, including batch processing, streaming, data quality, and pipeline orchestration
- Experience working with Kubernetes or similar container orchestration environments
- Solid software engineering fundamentals, including clean APIs, testing, and observability
- Experience supporting or working closely with research, data science, or ML teams
- Strong debugging and problem-solving skills
- Ability to manage multiple workstreams and communicate clearly with both technical and non-technical stakeholders
- Collaborative mindset with a genuine focus on enabling others to move faster
- Familiarity with PyTorch or other ML frameworks is nice to have
Benefits
Comp & perks- Equity packages - we want our success to be yours too
- Inclusive parental leave policy that supports all parents & carers
- An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more
- Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Pythondata pipelinesbackend servicesdata engineering patternsbatch processingstreamingdata qualitypipeline orchestrationdebuggingobservability
Soft Skills
problem-solvingcommunicationcollaborationability to manage multiple workstreamsfocus on enabling others
