Backend Software Engineer, Product
- Level
- Mid
- Work type
- On-site · Almaty
Not stated in the posting: salary, visa.
Open. First seen 17 October 2026.
What this role asks for
Standard for backend roles
AWS · Kubernetes · PostgreSQL · Python
Common in backend postings at any company.
Read against Higgsfield's other open postings on this site.
The team: Engineering & Product HIRES STEADILY
This role is one of 14 open in Engineering & Product. 7 were posted in the last 30 days and the oldest in April: a team that hires all year rather than one being built.
WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS
Generative AI (10 of 14), Python (9 of 14), BigQuery (5 of 14), SQL (5 of 14), AWS (3 of 14), CI/CD (3 of 14), Golang (3 of 14)
OTHER ROLES ON THIS TEAM
- Backend Software Engineer, Infrastructure posted Oct 17
- Backend Software Engineer, Trust & Safety (Anti-Fraud) posted Oct 17
- Backend Software Engineer, Agentic posted Oct 17
- Data Analyst, Antifraud posted Sep 30
- Data Scientist, Antifraud posted Sep 30
- Payments Analyst posted Sep 30
- AI Engineer posted Sep 8
- Data Science, Product Analyst posted Sep 7
- Data Scientist, Product posted Sep 7
- Lead GRC Engineer posted Aug 28
- Technical Program Manager posted Aug 19
- Frontend Engineer posted Apr 29
- Backend Engineer posted Apr 20
From the team and dates on Higgsfield's own job board.
About the role
Almaty, Kazakhstan · Trust & Safety Engineering
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.
What you'll actually do
- 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.
Must-haves
- 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.
Our tech stack
- 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.
Interview process
- 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.
What we offer
Competitive base in USD, equity, relocation support, and ownership of product experiences that directly shape how people create with AI.
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