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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer - Data Platform & Infrastructure - **Company:** Lumos AI Research Corp. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $160,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, Big Data, Cloud Computing, Collaborative Software, Data Infrastructure, Relational Databases, Programming Tools, Python (Programming Language), PostgreSQL, Machine Learning, Next.js, Software Engineering, Systems Architecture, TypeScript, Management of Software Versions, Web Application Frameworks, ReactJS, Large Language Models, Prompt Engineering, Model Validation, Caching, Information Technology, Free and Open-Source Software, Front End Software Development, Data Pipelines, Human in the Loop, Crud, Data Generation - **Published:** October 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=26230b0319f85573 ## About the Role * 5+ years of software engineering experience, or equivalent evidence of exceptional engineering ability. * Strong proficiency in TypeScript and Python, with experience building sophisticated, production-grade applications. * Experience with modern frontend frameworks such as React or Next.js, and a strong understanding of frontend architecture and application state management. * Solid backend engineering experience, including APIs, relational databases, asynchronous processing, and distributed services. * Experience designing complex data models, workflow systems, and scalable application infrastructure. * Strong understanding of PostgreSQL, queues, background jobs, caching, and cloud infrastructure. * Ability to build thoughtful user experiences for technically complex products. * Strong product intuition and the ability to translate ambiguous requirements into elegant, maintainable solutions. * Demonstrated ability to independently architect, ship, and iterate on complex systems. We'd be particularly excited if you have * Experience building data annotation, labeling, evaluation, or human-in-the-loop platforms. * Experience with AI training data pipelines, dataset curation, synthetic data generation, or model evaluation. * Familiarity with LLM APIs, structured generation, tool calling, prompt management, and AI-assisted workflows. * Experience building workflow orchestration systems, task queues, or configurable workflow engines. * Knowledge of data versioning, provenance, lineage, schema validation, and reproducibility. * Experience designing collaborative tools involving review, approvals, permissions, and complex state transitions. * Background in developer tools, internal platforms, scientific software, or other technically demanding products. * Experience working with healthcare, life sciences, or scientific datasets. The kind of person who thrives here We have a strong preference for intellectually curious, independent builders who enjoy difficult problems, move quickly, and take pride in creating exceptional software. Some backgrounds we're especially drawn to: * Former founders: People who have built companies or products from scratch, understand users deeply, and are comfortable owning problems without a predefined playbook. * Quantitative trading and high-performance engineering: Engineers accustomed to demanding technical environments, complex systems, and high standards for correctness and reliability. * Competitive mathematics and programming: People with backgrounds in IMO, IOI, ICPC, or similar competitions who enjoy difficult technical and mathematical challenges. * Computer science and philosophy: People who combine strong engineering skills with an interest in logic, reasoning, formal systems, and the foundations of intelligence. * Makers and independent builders: People who build ambitious side projects, open-source software, robots, hardware, or unconventional tools simply because they love creating things. These aren't requirements. They're examples of the intellectual curiosity, independence, and builder mentality we value. We're particularly interested in engineers who can think like product designers, reason like systems engineers, and execute like founders. ## Description We're looking for a Senior Software Engineer - Data Platform & Infrastructure to build the platform that powers the creation, curation, evaluation, and delivery of high-quality data for frontier AI models. You'll work on a sophisticated platform that coordinates domain experts, AI models, automated evaluation systems, and complex data production workflows. Our platform enables scientists, physicians, and other experts to create and validate training data, develop evaluation tasks, annotate model outputs, and collaborate with AI-assisted tools. Behind the scenes, it orchestrates multi-stage workflows, manages large datasets, tracks quality, and transforms expert knowledge into structured, machine-consumable outputs. This isn't a conventional annotation tool or CRUD application. We're building an intelligent data production system where human expertise, model-generated content, programmatic validation, and automated feedback loops work together at scale. You'll have significant ownership across the product and infrastructure stack, working directly with the founding team, researchers, and domain experts to turn complex data creation processes into intuitive, reliable, and scalable software. What Will You Do * Build the data creation platform: Design and implement core product features that enable experts to create, annotate, review, validate, and refine complex training and evaluation datasets. * Develop intelligent workflows: Build flexible, multi-stage pipelines combining human contributions, AI-generated content, automated evaluation, and expert review. * Build AI-native interfaces: Develop tools that enable experts to collaborate effectively with AI models, including AI-assisted annotation, content generation, feedback, and validation. * Engineer data infrastructure: Build robust systems for dataset storage, versioning, lineage, transformations, quality control, and delivery. * Develop task orchestration: Implement assignment, routing, dependencies, review queues, quality gates, and workflow automation across distributed teams of experts. * Build evaluation and quality systems: Integrate model-based judges, programmatic verifiers, consensus mechanisms, and quality metrics to ensure data reliability. * Create exceptional product experiences: Build fast, intuitive interfaces for complex scientific and healthcare workflows, balancing flexibility with simplicity. * Integrate frontier AI models: Develop infrastructure for model inference, structured generation, prompt execution, and automated feedback loops. * Scale the platform: Improve performance, reliability, observability, and developer experience as data volume, workflow complexity, and expert participation grow. * Own systems end to end: Drive technical decisions from product design and system architecture through implementation, deployment, and iteration.