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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
As Product Engineer, Sciences, you will build full-stack software for scientists and technical users, working at the intersection of engineering and laboratory science. You'll partner closely with the Sciences team and end users to identify problems, make product decisions, and iterate rapidly. This is a hands-on role spanning frontend, backend, and data infrastructure, with ownership from initial prototype through production deployment and ongoing support.
Key responsibilities include:
- Developing full-stack products: Build interfaces, backend services, and data models that streamline scientific workflows. Carry work from useful first release into reliable daily operation.
- Understanding laboratory work: Work directly with scientists to understand protocols, instruments, experimental records, and practical constraints. Translate that understanding into clear requirements and usable software.
- Connecting systems and data: Build integrations, imports, and exports. Preserve context and traceability while handling incomplete records, validation failures, and changing input formats.
- Owning production quality: Test, deploy, monitor, and support your work. Maintain access controls, debug failures, and improve performance based on real usage patterns.
- Making product decisions: Identify the most useful next improvement, test it with users, and communicate tradeoffs. Adapt to unfamiliar problems and collaborate with specialists when deeper expertise is needed.
You should have shipped and maintained production software across frontend, backend, and database layers, and can debug across those boundaries. Lab fluency is essential—gained through either bench experience or sustained work building scientific software alongside experimental scientists. You build clear, interactive interfaces and can translate complex requirements into practical workflows. You're comfortable building reliable APIs, working with databases and file storage, and investigating errors in data-processing workflows. You work directly with users, make scope decisions under ambiguity, and follow through after release.
Strong signals include: products used regularly by scientists or laboratory operators with improvements shaped by their feedback; experience with scientific records, instrument integrations, or data-intensive review workflows; and practical use of AI tools in products or engineering with attention to validation and failure handling.
REQUIREMENTS:
- Full-stack engineering: shipped and maintained production software across frontend, backend, and database
- Lab fluency: understanding of experimental workflows, protocols, controls, and sources of variability through bench work or sustained scientific software work
- User-facing development: ability to build clear, interactive interfaces and translate complex requirements into practical workflows
- Backend and data skills: building reliable APIs, working with databases and file storage, investigating errors in data-processing workflows
- Product judgment and ownership: working directly with users, making scope decisions under ambiguity, following through after release
