Staff Software Engineer, Twelve Labs

• Significant software engineering experience building and operating mission-critical backend systems at scale
• Experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, infrastructure as code, or container orchestration
• Strong interest in ML inference — you want to understand how models work, even if your primary contribution is the infrastructure around them
• Ability to design highly observable systems that operate reliably at scale across multiple environments
• Autonomy and ownership — you take problems end to end with a bias toward high-impact work

Desirable

• You'll own the infrastructure and integration layer that makes TwelveLabs models available on partner platforms
• This is everything outside the model itself: how model containers are packaged, validated, and deployed; how API surfaces are designed and maintained per platform; how requests are routed; and how we ensure production reliability across fundamentally different cloud environments
• You'll work closely alongside our Science, Product, and ML Engineering teams to align the model and product roadmap for effective platform integrations
• Your domain is the external model orchestration — you need to understand how model components function (to make good integration decisions), but you won't be optimizing the models themselves
• Your work accelerates the ability to reliably ship new model versions and features to users across all platforms
• Design and build infrastructure that deploys TwelveLabs models across multiple cloud and data platforms, accounting for differences in compute hardware, networking, APIs, and operational models
• Own direct integrations into partner products — implementing the orchestration, data flow, and API surfaces that connect TwelveLabs models to partner-side functionality
• Design and evolve CI/CD automation systems — including validation and deployment pipelines that reliably ship new model versions across platforms without regressions
• Design interfaces and tooling abstractions across platforms that enable consistent deployment, reduce per-platform complexity, and scale as we add new partners
• Implement API-level features and changes that require understanding model component behavior — routing, request handling, response formatting — without modifying model internals
• Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across platform deployments
• Analyze observability data across platforms to identify performance bottlenecks, cost anomalies, and regressions — and drive remediation based on production workloads
• Collaborate with platform partner engineering teams to resolve operational issues, align on API contracts, and stand up end-to-end serving on new platforms