We are looking for a talented Product Engineer to build the scalable features, interactive environments, and user-facing capabilities. In this role, you will bridge the gap between high-performance backends and intuitive user experiences, allowing our customers to seamlessly configure, simulate, and analyze complex workflows. Your primary focus will be on engineering robust, user-centric product features that translate advanced AI/ML capabilities into a seamless, high-performance web application.
End-to-End Quality Ownership: Implement comprehensive automated testing strategies across the entire feature lifecycle—including rigorous unit, integration, and end-to-end (E2E) testing—to guarantee that complex simulation UI and backend states never regress.
Scalable Product Features: Architect, build, and maintain robust, user-facing features using Python (FastAPI) and modern frontend frameworks (React/Next.js) to deliver a seamless end-to-end user experience.
High-Performance Backends: Design and optimize asynchronous backend services capable of handling intensive workloads, coordinating heavy simulation tasks, and managing real-time data streaming.
SimLab Core Workflows: Own the execution and orchestration layer, ensuring that user-configured simulation environments and data pipelines run deterministically, resiliently, and at scale.
Data & State Management: Design and optimize both SQL and NoSQL data layers to manage complex user configurations, log high-volume simulation metrics, and retrieve historical telemetry data efficiently.
API Design & Integration: Build clean, versioned, and intuitive APIs that connect SimLab’s frontend with core AI/ML orchestration engines and external data sources.
Edge-Case Resilience: Proactively architect error-handling mechanisms and defensive code patterns to ensure our products handle unpredictable simulation inputs, high concurrency, and massive datasets without degrading the user experience.
Product Observability: Instrument deep telemetry, logging, and error-tracking across the application stack to monitor feature health in production, quickly isolating and resolving quality bottlenecks before they impact users.
Product Engineering Mindset: Proven experience in a Full-Stack or Product Engineering role, with a passion for building highly interactive, reliable, and user-facing SaaS products (ideally in the developer tool, simulation, or AI space).
Obsession with Reliability: A strong engineering philosophy centered on code correctness and product stability; you don't consider a feature "done" until it is fully tested, documented, and resilient against edge cases.
Robust Backend Expertise: Strong software development skills in Python (FastAPI) with a deep understanding of asynchronous programming, concurrent systems, and distributed task queues (e.g., Celery, Redis).
Data Layer Knowledge: Solid understanding of relational and non-relational databases (SQL/NoSQL) and query optimization, particularly for handling large-scale simulation outputs or time-series data.
AI/ML Familiarity: Prior exposure to AI/ML workflows, training pipelines, or NLP/LLM applications. Experience or a strong interest in interfacing products with Reinforcement Learning (RL) or simulation environments is highly desirable.
Engineering Culture: A strong champion of clean code, comprehensive testing (TDD), CI/CD best practices, and building highly maintainable, scalable architectures.
Adaptability: Prior experience in a fast-paced startup or top-tier tech environment where you’ve successfully taken features from ambiguity to production-grade deployment.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.