Sr Stack Engineer

We are building a high-performance, distributed AI orchestration platform that processes complex, multi-step pipelines s is not a typical SaaS role; you will own architecture decisions, system design, and delivery velocity. We ship one production-grade feature every 2 weeks. You will use AI-assisted development tools (Cursor, Windsurf, Claude Code, or Antigravity) daily as your core workflow, not as a novelty. These tools are how we achieve senior-engineer velocity without sacrificing quality.

The core responsibilities for the job include the following:

Expert-level distributed systems knowledge you will own:

• Async job orchestration (Temporal.io ) and event-driven pipelines.
• Multi-service coordination at scale with service-oriented architecture.
• Eventual consistency, the CAP theorem, and scaling patterns (sharding, partitioning, and replication).
• Production Redis experience with pub/sub, caching, and cluster management.

Advanced Postgres and Supabase (production level):

• Complex schema design, query optimization, indexing.
• Row-level security, triggers, and stored procedures.
• Multi-tenant data isolation, migrations at scale.

AI pipeline reliability design systems with:

• Idempotency, resumable workflows, retry strategies.
• Cost tracking and spend guardrails.
• Clear logs and error states.
• Full-stack capability owns features end-to-end (schema API UI deployment), but with deep backend expertise. You spend 70% of your time on backend/infrastructure and 30% on frontend.

System design and architectural thinking:

• Architect solutions for 10M+ events/day without hand-holding.
• Design with clarity, identifying trade-offs before implementation.
• Think in terms of constraints, dependencies, and long-term maintainability.

Spec-driven development:

• Read specs thoroughly and flag ambiguities early.
• Design APIs and schemas that match the spec intent.
• Use specs to guide implementation and AI generation.

Production-grade AI-assisted development (must have real shipping experience):

• Deep mastery of at least one:
  Cursor, Windsurf, Claude Code, or Antigravity.
• Expert context management structuring prompts, codebase context, and specs for AI tools.
• Shipped production features using AI tools without sacrificing quality or introducing regressions.
• The reality:
  Excellent engineers with AI ship faster and better. Poor engineers with AI introduce more bugs.

Senior engineering mindset:

• Think about trade-offs, not just make it work.
• Flag architectural risks before they become production incidents.
• Mentor through code and design.

The core requirements for the job include the following:

Strong advantages:

• Experience of server-side video generation using AI models (Veo, Kling, Hailu, Seedance, Sora, Wan, etc.).
• Experience of concurrent video/image generation using heavy AI models.
• Strong knowledge of video generation concepts (text-to-video, image-to-video, and image-to-video with reference images).
• Experience of image generation using AI models (Nano Banana, Wan, etc.).
• Google Vertex AI experience.
• < 1 week feature shipping cadence.
• UGC domain knowledge workflows, asset pipelines, AI-assisted tools (Higgsfield, Speel), video generation.

Tech stack proficiency:

• Runtime:
  Node.js .
• Frontend:
  Next.js (App Router), Type Script (strict mode).
• Backend:
  NestJS, Type Script (strict mode).
• Cloud: GCP (Vertex AI), AWS (EC2 EKS, RDS, Lambda).
• Deployment:
  Vercel, Docker, Kubernetes.
• CI/CD:
  Git Hub Actions.
• AI Models:
  Claude, Gemini, Vertex AI, Veo, Kling, Hailu, Seedance.
• Testing:
  Playwright.