Automation Engineer,Materials Research Science Job Redmond area,Washington USA,Engineering

Summary

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.

Required

Skills

Automation Engineer, Materials Research Science 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

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

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

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…