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Operations Platform Engineer

United States, Redmond Employment contract 208000.00 - 289000.00 USD / Year · Job Posted May 17, 2026
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Job Description

Meta is seeking a highly motivated and experienced Operations Platform Engineer to own and evolve the robotics test and data infrastructure that underpins motor validation, experimentation, and downstream analysis. We are building next-generation robotic systems that operate in the real world and generate large volumes of high-fidelity data. As these systems scale, we need robust software infrastructure to ensure that motor control, testing, telemetry, and data pipelines are repeatable, observable, and usable across teams. This is a hands-on, builder role at the intersection of robotics hardware, system software, and data platforms. You’ll help research and engineering teams scale beyond one-off solutions by turning early prototypes into reliable, extensible systems that increase test throughput, improve data quality, and accelerate iteration.

Job Responsibility

  • Design and build motor and actuator test infrastructure, including control loops, data capture, and validation tooling
  • Develop and standardize repeatable test stations that scale across hardware variants, labs, and teams
  • Define and implement telemetry schemas and data contracts for robotic systems (commands, feedback, environment, failures), ensuring consistency across programs
  • Build time-synchronized data pipelines to support debugging, replay, offline analysis, and training workflows
  • Establish observability standards for robotic systems, including metrics, logging, diagnostics, anomaly detection, and dashboards
  • Partner closely with robotics hardware, firmware, research, safety, and operations teams to ensure systems are reliable, safe, and extensible
  • Identify and eliminate bottlenecks in data quality, test throughput, and system reliability as usage scales to more teams and more robots
  • Drive architecture decisions that balance rapid experimentation with long-term maintainability, operational robustness, and scalability
  • Support fleet and lab validation workflows by enabling consistent test execution across platforms (e.g., Lithium, Ber, Boron, Carbon, Aloha, Mimmic, Trossen)
  • Contribute to system-level failure understanding by enabling instrumentation and workflows that accelerate failure triage and root cause analysis

Requirements

  • 7+ years of experience in software engineering, systems engineering, robotics engineering, or related fields
  • 3+ years of experience working close to hardware, including motors, sensors, actuators, embedded systems, and/or embedded Linux environments
  • Proven ability to design and build test frameworks or infrastructure for physical systems (labs, manufacturing tests, reliability rigs, end-of-line, or similar)
  • Experience building data ingestion pipelines for high-frequency and/or real-time telemetry (including time sync, buffering, backpressure, and schema evolution)
  • Systems engineering fundamentals: APIs, data schemas, failure modes, reliability, operational discipline and maintainable interfaces
  • Ability to operate effectively in ambiguous, fast-moving environments with evolving requirements
  • Proven communication and collaboration skills across hardware, software, and research disciplines

Nice to have

  • Experience in industrial robotics, automation, embedded Linux, or real-time systems
  • Experience with robotics data formats, replay systems, simulation pipelines, or log-based debugging at scale
  • Familiarity with observability tooling and practices (metrics, logging, tracing, dashboards, alerting)
  • Experience supporting ML or research teams through infrastructure (data capture, labeling support, dataset generation, evaluation pipelines) rather than model development
  • Prior experience bringing prototype systems into scaled, multi-team usage, including documentation, onboarding, and operational support
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

What we offer

  • bonus
  • equity
  • benefits

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