Product Engineer, Sciences

Mecka•New York, NY

About The Position

Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate 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. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. As Product Engineer, Sciences, you'll build software for scientists and technical users. Bring the engineering ability to ship useful products and the lab fluency to understand the work behind them. You'll work closely with the Sciences team and users to identify problems, make product decisions and iterate quickly. This is a hands-on role across frontend, backend and data, with ownership from the first prototype through production use. Lab fluency can come from bench experience or sustained work building scientific software alongside experimental scientists.

Requirements

Full-stack engineering: You've shipped and maintained production software across the frontend, backend and database, and can debug across those boundaries.
Lab fluency: You understand experimental workflows, protocols, controls and sources of variability through bench work or sustained scientific software work with experimental users.
User-facing development: You build clear, interactive interfaces and can turn complex requirements into practical workflows that users can understand.
Backend and data: You can build reliable APIs, work with databases and file storage, and investigate errors in data-processing workflows.
Product judgment and ownership: You work directly with users, make scope decisions under ambiguity and follow through after release.

Nice To Haves

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.
Practical use of AI tools in products or engineering, with attention to validation and failure handling.

Responsibilities

Build full-stack products: Develop interfaces, backend services and data models that make scientific workflows easier to use.
Carry work from a useful first release into reliable daily operation.
Understand laboratory work: Work directly with scientists to understand protocols, instruments, experimental records and practical constraints.
Translate that understanding into clear requirements and usable software.
Connect systems and data: Build integrations, imports and exports.
Preserve context and traceability while handling incomplete records, validation failures and changing input formats.
Own production quality: Test, deploy, monitor and support your work.
Maintain access controls, debug failures and improve performance based on real usage.
Make product decisions: Identify the most useful next improvement, test it with users and communicate tradeoffs.
Adapt to unfamiliar problems and work with specialists when deeper expertise is needed.