Research Engineer - Automated Lab Development

Meta·Redmond, Washington, United States·posted 46d ago · last seen 38m ago

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About interviewing at Meta

Recruiter screen, a technical screen (60-minute asynchronous challenge or paired live coding), a short culture-fit questionnaire, then a virtual onsite of about four rounds: fast-paced coding with multiple problems per round, system design scoped to level, a lighter behavioral, and an AI-enabled coding round assessing how you work with a coding assistant.

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Description

About

At Meta, we are looking for an experienced and motivated engineer to define and drive the software orchestration and physical infrastructure for our autonomous, "self-driving" battery research lab. In this role, you will lead the technical build-out of connected, multi-station automated workcells and guide the architecture for APIs and data pipelines required to bridge laboratory hardware with advanced machine learning pipelines. Collaborating closely with a cross-functional team of materials scientists and ML engineers, your orchestration platform will directly accelerate the closed-loop discovery and deployment of next-generation battery technologies for next-generation devices.

Responsibilities

  • Define the overall technical roadmap and detailed system architecture to build an autonomous, automated, end-to-end battery research lab, seamlessly integrating both commercially available instruments and custom hardware solutions
  • Identify gaps where commercial automation solutions fall short. Define strategies for external partner engagement and directly lead the co-development of custom automation approaches with third-party vendors. Own build-out of partner automation solutions within Meta labs. Drive or directly develop application-specific customizations and extensions of automation solutions
  • Guide development of software by internal and/or external partners to operate individual system components, orchestrate the full system, and provide resulting data to researchers
  • Translate the needs of internal research scientists into actionable hardware and software engineering specifications for the autonomous lab system, ensuring continuous alignment with core research objectives

Minimum Qualifications

  • Bachelor's degree in Robotics, Mechatronics, Electrical Engineering, Computer Science, Mechanical Engineering, or a related technical discipline
  • 8+ years of hands-on experience designing, scaling, and deploying automated laboratory systems, robotic workcells, or complex industrial testing/characterization hardware
  • Experience setting requirements for and using software to control physical hardware, manage device state machines, sequence multi-instrument workflows. Experience setting requirements for custom APIs, web services, or serial communication protocols to bridge data and control pathways between different vendor instruments
  • Experience managing technical definitions, timelines, and deliverables with third-party automation vendors, system integrators, or external OEM partners
  • Experience collaborating with cross-functional teams (e.g., research scientists, software engineers, hardware engineers) to translate scientific experimental requirements into engineering specifications Master's or Ph.D. in Robotics, Mechatronics, Computer Science, or a related field with a thesis or research focus on automated physical systems
  • Familiarity with modern laboratory connectivity standards and middleware protocols
  • Experience integrating automated hardware with Electronic Laboratory Notebooks (ELNs), Laboratory Information Management Systems (LIMS), or centralized cloud data warehouses
  • Experience with battery and materials characterization techniques, including electrochemical cell characterization, cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), and Fourier-transform infrared spectroscopy (FTIR)
  • Experience with industrial lab orchestration/scheduling software or data workflow management tools
  • Direct experience working on automated high-throughput screening (HTS) or "closed-loop" autonomous discovery systems where hardware execution loops were driven by algorithmic/ML inputs

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