Description :
We're looking for an exceptional Software Engineer (Full-Stack) to help architect and scale the systems that power GEO from high-throughput backend infrastructure to intuitive front-end analytics experiences. You'll design distributed services that process large-scale simulation reinforcement learning experiments, build APIs for large-scale data-centric workflows, and craft beautiful, data-rich interfaces that make complex model behaviors interpretable. This is a highly cross-functional role working alongside ML engineers, researchers, and product designers to transform cutting-edge research into production-grade systems.
Responsibilities :
Backend and Platform :
- Optimise compute, storage, and inference workflows for large-scale simulation reinforcement learning experiments.
- Architect and build distributed systems that power the company's GEO engine and data pipelines.
- Develop APIs and services that connect retrieval, ranking, and evaluation layers.
- Implement observability, monitoring, and CI / CD tooling for reliability and scalability.
Frontend and UX :
Build intuitive dashboards and tools that visualise GEO metrics, LLM behaviours, and visibility analytics.Collaborate with design and ML teams to develop real-time visualisations for telemetry, ranking performance, and search simulation results.Implement responsive, high-performance interfaces using React, TypeScript, and Next.js.Maintain a consistent design system that communicates complex data clearly and beautifully.Requirements :
3+ years of experience in software engineering (backend, frontend, or full-stack).Proficiency in Python, Go, or Node.js for backend development.Strong experience with React, TypeScript, or Next.js on the frontend.Familiarity with distributed systems and cloud platforms (AWS, GCP) and orchestration tools (Docker, Kubernetes).Strong understanding of databases (PostgreSQL, Redis) and data pipelines (Kafka, Airflow, etc.).Comfort working across the stack and collaborating closely with ML and product teams.Preferred :
Experience with data visualisation libraries (e.g., D3.js, Plotly, or Recharts).Familiarity with LLM or ML product interfaces, experiment dashboards, or model evaluation UIs.Background in building SaaS or analytics products for data-intensive domains.Passion for elegant design and communicating complex systems visually.(ref : hirist.tech)