Staff Software Engineer, Platform Integrations

Twelve Labs is pioneering the development of multimodal foundation models for video comprehension. The Staff Software Engineer, Platform Integrations will own the infrastructure for integrating these models with partner platforms, ensuring production reliability and effective deployment across various cloud environments.

Responsibilities:

• 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

Requirements:

• 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
• Direct experience working with cloud provider partner teams to scale infrastructure or products across multiple platforms — navigating differences in networking, security, billing, and managed service offerings
• Background building platform-agnostic tooling or abstraction layers that work across cloud providers
• Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments
• Familiarity with ML inference optimization, batching, caching, and serving strategies
• Experience with ML infrastructure including GPUs, TPUs, Trainium, or other AI accelerators
• Background designing CI/CD systems that automate deployment and validation across cloud environments
• Proficiency in Python or Go