Backend Software Engineer, Product

• Deep database expertise: data modeling, indexes, query plans, partitioning, transactions, and locking. You diagnose production bottlenecks and understand the trade-offs behind your fixes.
• Strong architecture judgment: service boundaries, APIs, asynchronous workflows, caching, and failure handling. Hands-on Kubernetes and CI/CD experience, from design and testing to deployment and debugging.
• A track record of shipping and operating backend systems across multiple projects, including high-traffic workloads. You can explain your decisions and measurable results.
• API security: authentication, object-level authorization, and tenant isolation. You prevent IDOR in reads and writes, including resource IDs in request bodies, and test access across users and workspaces.
• You've built or studied social networks and collaborative tools, and can explain design trade-offs in feeds, follow graphs, notifications, sharing permissions, real-time sync, and concurrent edits.
• High agency and ownership: you find worthwhile problems, propose improvements, remove blockers, and follow through without waiting for assignments.
• Low ego: you welcome direct feedback, admit mistakes, change your mind with evidence, and help wherever the team needs you.
• Ambition and optimism: hard goals and rapid growth energize you. You challenge assumptions constructively and commit to making ambitious ideas work.
• Energy for an intense pace, shifting priorities, and urgent launches or production issues that can require work outside regular hours.

Why this role exists

Higgsfield builds tools for AI creators: creative workflows, collaborative projects in Cinema Studio, and a community for sharing work. We need a backend engineer who combines deep database and architecture expertise with the initiative to own product outcomes, from the first idea to production.

• Build AI creation workflows, Cinema Studio collaboration, and community features: sharing, discovery, contests, profiles, and follows.
• Own features and bugs through release and follow-up. Define API contracts and coordinate frontend work to deliver the complete feature.
• Deliver responsive UX: live progress, timely updates, and clear failure recovery, even when it takes more engineering effort.
• Remove database and service bottlenecks under high traffic; improve correctness, latency, and reliability.
• Define success metrics, build dashboards and alerts, and track adoption and system health after launch.

• Languages and frameworks: Python (FastAPI), Go (Gin), and TypeScript (React/Next.js on the frontend).
• Data: PostgreSQL and Redis; analytical databases such as BigQuery or ClickHouse.
• Messaging: Kafka, Amazon SQS, and RabbitMQ; event-driven services and asynchronous workers.
• AWS: S3, DynamoDB, ECR, and Secrets Manager.
• Infrastructure: Docker, Kubernetes on Amazon EKS, Helm, Argo CD, and GitHub Actions.
• Observability: Prometheus, Grafana, OpenTelemetry, and Sentry.

• Recruiter screen: role expectations, logistics, and fit.
• Technical interview: a difficult system you shipped, what you personally decided and built, and how your work made the team more capable without leaving it dependent on you.
• Additional technical interview, if needed: a deeper discussion of technical skills and open questions.
• Product fit interview: turn a user problem into a small first release, decide what to leave out, and explain how feedback would change the solution.
• Offer.

Competitive base in USD, equity, relocation support, and ownership of product experiences that directly shape how people create with AI.