About Origin Origin (previously 10xConstruction) is building general-purpose autonomous robots for US construction to tackle rising costs, safety risks, and labour shortages.
Our modular, multi-trade platform combines purpose-built hardware with real-time site intelligence to navigate complex environments and execute tasks with precision.
Trained in high-fidelity simulation and already deployed on live sites, our robots deliver 5x faster execution, 250%+ margin expansion, and significant cost savings.
Join India's most talent-dense robotics team consisting of individuals from IITs, Stanford, UCLA, etc.
About the Role You will own the quality and performance of every tool the robot operates: spray guns, sanders, and future finishing tools used in indoor construction.
This means running rigorous experiments to find the right operating parameters, characterising defects, building mitigation strategies, and producing the structured datasets the AI team needs to close the loop on autonomous tool use.
You will lead a cross-disciplinary team spanning manipulation, perception, AI, and mechanical engineering, and you will build the physical testing infrastructure (sample panels, mock environments, instrumented rigs) that makes all of this possible.
Key Responsibilities Design and execute structured experiments to identify optimal tool parameters (pressures, speeds, distances, feed rates, material combinations) for each finishing application, with full documentation of methodology, controls, and results.
Characterise defect modes (runs, sags, orange peel, uneven sanding, missed coverage) across applications; develop and validate mitigation strategies with measurable pass/fail criteria.
Define and enforce a scientific data collection protocol for every tool-use experiment, ensuring datasets are labelled, versioned, and structured for direct consumption by the AI team.
Lead the cross-disciplinary applications team (manipulation, perception, AI, mechanical) to deliver validated tool-use capabilities for spraying, sanding, and subsequent indoor construction applications.
Specify, procure, and commission the testing infrastructure: sample substrates, mock wall assemblies, spray booths, dust extraction, and instrumented measurement rigs.
Build and maintain a library of standardised test samples and real-world reference environments that represent the range of conditions encountered on active construction sites.
Own the end-to-end operations of the applications lab: scheduling, safety protocols, consumables inventory, and equipment calibration.
Required Qualifications and Skills 5+ years of hands-on experience in a role involving process parameter optimisation for coating, spraying, painting, sanding, or similar surface-finishing operations, in either an industrial, automotive, or research setting.
Demonstrated ability to design controlled experiments, collect high-quality data, and draw defensible conclusions.
Formal training or equivalent rigour in experimental design and statistical analysis.
Working proficiency with robot arms through their pendant/teach interface.
Comfortable jogging, programming waypoints, and running routines on industrial manipulators.
Functional knowledge of electromechanical systems: pneumatics, spray equipment, motor-driven tools, basic sensor integration.
Scripting ability in Python sufficient to automate data logging, instrument control, and basic analysis pipelines.
Background in materials science or a closely related discipline, with an understanding of how substrate properties, coatings, abrasives, and environmental conditions interact.
Preferred Experiences Prior experience generating training or evaluation datasets for machine learning or computer vision teams, with an appreciation for what makes data useful for model development.
Familiarity with construction finishing trades (drywall, plastering, painting) and the quality standards applied on commercial job sites.
Experience setting up a lab or test facility from scratch, including layout, procurement, safety, and SOPs.
Track record of leading a small, cross-functional technical team toward a shared deliverable.
