Automation Engineer, Materials Research Science

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.

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

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…