Meta Reality Labs is seeking an engineer to advance materials research capabilities for next-generation wearables hardware. In this role, you will design, build, and operate the automation backbone of an autonomous materials discovery lab — connecting AI agents, robotic work-cells, and scientific instruments into a seamless, closed-loop pipeline. Working at the intersection of lab automation, agentive AI, and computational materials science, this role translates scientific workflows into production-grade software that compresses a discovery cycle from years into weeks, accelerating the development of novel materials for next-generation wearable devices and robotics.
Responsibilities
Define the long-term technical roadmap for laboratory automation systems, integrating robotic sample handling, automated metrology instruments, and data acquisition pipelines Architect and own the end-to-end automation infrastructure for high-throughput materials characterization workflows, including optical, mechanical, and electrical property testing of wearable device materials Collaborate with scientists, hardware engineers, and product teams to translate experiments and lab workflows into clear integration specifications, data models, and scalable automation solutions Work with integrators and vendors to design, build, and commission automated workcells for materials R&D (process development, characterization, property testing, etc.) Build and maintain middleware services that connect instruments, robots, and sensors to laboratory information management systems Develop instrument drivers and automation scripts that generate command sequences and invoke vendor APIs/SDKs to orchestrate lab workflows end-to-end Collaborate with AI and data scientists to tightly integrate the autonomous lab with LLM-based multi-agent systems for experiment planning, analysis, and decision-making Design and implement data pipelines that capture, validate, and store experimental metadata to ensure data integrity and reproducibility across the discovery pipeline Evaluate and benchmark automation performance — measuring throughput, reliability, error rates, and turnaround time of automated experimental workflows Contribute to internal tooling, documentation, and best practices that enable the broader team to leverage automation capabilities Drive the adoption of design-of-experiments methodologies and statistical process control within automated materials screening workflows Define standards and best practices for automation system reliability, calibration, and data integrity across the materials research organization Provide technical guidance to other engineers on automation architecture decisions, instrumentation integration patterns, and software design for laboratory systems Evaluate and integrate emerging laboratory automation technologies, robotics platforms, and scientific instrumentation relevant to materials research
Qualifications
Ph.D. degree in Electrical Engineering, Computer Science, Mechanical Engineering, Control Engineering, Materials Science, or relevant field, and/or equivalent practical experience 6+ years of experience in lab automation, systems integration, or industrial automation software and/or relevant technical experience Proficiency in Python, with experience writing production-quality automation and integration code Hands-on experience with lab automation platforms (e.g., liquid handlers, robotic arms, automated characterization tools) Experience with laboratory information management systems, electronic lab notebooks, or manufacturing execution systems Demonstrated ability to translate scientific or manufacturing workflows into reliable, automated processes Experience architecting scalable automation platforms for materials characterization or physical science research environments Experience with statistical analysis and data pipeline design for high-throughput experimental datasets A track record of commissioning or bringing up complex lab, pilot, or manufacturing equipment Familiarity with APIs, databases, and enterprise software integration patterns Experience defining automation strategy and technical standards at an organizational level within a research or advanced hardware development environment Familiarity with computational chemistry or materials science tools (DFT, MD, LAMMPS, ASE) and high-performance computing (HPC) environments Experience with retrieval-augmented generation (RAG), knowledge graphs, or scientific literature mining in the context of lab systems Publications or demonstrated accomplishments recognized in the field of laboratory automation or materials informatics Experience with materials relevant to wearables hardware, such as optical coatings, waveguide materials, display substrates, or flexible electronics Experience integrating robotic platforms with laboratory information management systems (LIMS) or material databases Experience integrating AI/ML models or LLM-based agent frameworks into physical lab workflows Experience with data historians, or real-time supervisory dashboards Knowledge of industrial communication protocols Familiarity with design-of-experiments frameworks and machine learning approaches applied to accelerated materials discovery