United States · On-site
$150k–$250k/yr
About the Role 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 backend…
Skills: Python, FastAPI, React, Next.js, SQL
Bengaluru, Karnataka, India · Remote OK
About the Role We are looking for a Machine Learning Engineer who sits at the intersection of robust system design, full-stack engineering, and MLOps. In this role, you won't just build features; you will architect highl…
Skills: Python, FastAPI, MLOps, AWS, Docker
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The role involves building scalable, user-facing features and high-performance backends to translate AI/ML capabilities into a web application. Key duties include owning end-to-end quality, designing API integrations, and managing complex data layers for simulation workflows.
Candidates need strong full-stack experience with Python (FastAPI) and modern frontend frameworks, along with expertise in SQL/NoSQL databases. A degree in Computer Science or Engineering and familiarity with AI/ML or simulation environments are required.
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.
Shipping safer, smarter, and more reliable AI into enterprise environments.
Collinear AI builds simulation labs where AI agents learn enterprise work before going to production. AI agents are inevitable. But they can't be trained or tested on the real world. You can't fix what you can't reproduce. They need a world to practice in -- that's what we build. Our labs give agents enterprise APIs, realistic users who push back and change their minds, tasks with real-world ambiguity, and verifiers that score what actually matters. So you can stress-test your agents before production and continuously improve them after. We work with frontier AI labs, and F500 enterprise AI teams including ServiceNow, IBM, HUMAIN, and Zoho.
Offices: Mountain View, US · 530 Lakeside Dr, Ste 100, Sunnyvale, California 94085, US
Shipping safer, smarter, and more reliable AI into enterprise environments.
Collinear AI builds simulation labs where AI agents learn enterprise work before going to production. AI agents are inevitable. But they can't be trained or tested on the real world. You can't fix what you can't reproduce. They need a world to practice in -- that's what we build. Our labs give agents enterprise APIs, realistic users who push back and change their minds, tasks with real-world ambiguity, and verifiers that score what actually matters. So you can stress-test your agents before production and continuously improve them after. We work with frontier AI labs, and F500 enterprise AI teams including ServiceNow, IBM, HUMAIN, and Zoho.
Offices: Mountain View, US · 530 Lakeside Dr, Ste 100, Sunnyvale, California 94085, US
