Company Description
We a platform that enhances visibility across AI search ecosystems such as ChatGPT, Perplexity, and other generative search interfaces. Our goal is to help businesses ensure their content is accurately represented, discoverable, and optimized for AI-driven discovery. The platform combines backend intelligence, data integration, and automation to continuously improve how information surfaces within generative AI results.
We are looking for an experienced Full-Stack Engineer with a strong backend and systems orientation to work across the core platform. You will be responsible for backend architecture, cloud infrastructure, and end-to-end system reliability, while also contributing to frontend components where needed. This role sits at the heart of the platform and involves close collaboration with machine learning, product, and design. We are a small, fast-growing team, and this role sits at the heart of the platform. We are not a typical 95 organization. The work is intense, hands-on, and highly impactful. If you want to grow quickly, create real systems, and build a product from the ground up, you'll feel at home here.
Role Description
Platform & Backend Architecture
- Design, build, and maintain core backend services, APIs, and data flows that power the platform.
- Own system architecture decisions, ensuring services are modular, scalable, and maintainable.
- Translate complex product and data requirements into robust backend implementations.
Cloud, Infrastructure & Networking
- Design, deploy, and operate cloud infrastructure (e.g., AWS, GCP, or Azure), with attention to scalability, cost efficiency, and fault tolerance.
- Manage service-to-service communication, authentication layers, rate limiting, and secure data transmission across distributed systems.
- Work with networking concepts such as load balancing, VPCs, DNS, firewalls, and traffic routing as part of platform design.
Data, Integrations & Automation
- Maintain database integrity, performance, and scalability across production systems.
- Build and manage background jobs, queues, webhooks, and data pipelines.
- Integrate third-party services, AI platforms, and internal machine-learning outputs into the core product.
Reliability, Performance & Security
- Monitor system health, performance, and availability using logging, metrics, and alerting.
- Proactively identify and resolve stability, performance, and security issues.
- Implement best practices around authentication, authorization, access control, and data protection.
Product & Frontend Collaboration
- Contribute to frontend components when needed, ensuring smooth and secure user flows.
- Fix bugs and optimize flows across the full user journey, from onboarding to conversion.
- Collaborate closely with product, design, and ML teams to deliver cohesive features end-to-end.
Delivery & Operations
- Contribute to CI/CD pipelines, environment management (dev/staging/prod), and automated testing.
- Take features from development through production deployment and ongoing maintenance.
- Help shape engineering standards, documentation, and internal tooling as the platform evolves.
Qualifications
- 5+ years of experience in full-stack or backend engineering, with clear ownership of production systems.
- Strong experience designing and building backend systems, APIs (REST and/or GraphQL), and data-driven services.
- Proven experience managing databases (e.g., PostgreSQL, MySQL, MongoDB), with a focus on correctness, performance, and scalability.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure).
- Solid understanding of networking fundamentals as applied to modern cloud systems (HTTP, TCP/IP, DNS, load balancing, security).
- Experience designing systems for reliability, scalability, and real-world production workloads.
- Ability to reason about architecture trade-offs and make pragmatic technical decisions.
- Comfortable working in fast-moving, early-stage environments where ownership matters.
Nice to Have
- Experience with containerization and deployment tooling (Docker, Kubernetes, or managed equivalents).
- Exposure to AI-driven products, data pipelines, or ML-adjacent systems.
- Experience building internal tools, dashboards, or admin systems.
- Prior work on platforms with external integrations or API-first architectures.
