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
Minimum 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
Preferred Qualifications
• 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
