Job description

At Meta, we're building the future of human connection and the technology that enables it, continuously inventing for the next generation of experiences. To move toward advanced machine intelligence, we're enabling AI to go beyond vision and interact with the physical world. As we move closer to a future with intelligent robots and advanced AI models, we're hiring talent across robotics hardware, system software, machine perception, and artificial intelligence. You'll tackle open problems in robotics and AI, with the opportunity to shape new ways people connect around the world. Hardware fails at the seams — where mechanical, electrical, firmware, and software meet, and again where a working prototype meets a repeatable build process. This role owns both. You will bring up new hardware in the lab, then carry it forward into a manufacturable, testable, scalable product: defining the build and test process, standing up the stations that verify it, and driving yield, throughput, and first-pass quality as volumes grow. It is a deliberately blended role for someone who can debug a one-off unit on the bench in the morning and improve a process that has to work on the hundredth unit in the afternoon. Lead bring-up of new platforms, actuator generations, and subassemblies: power-on, firmware flashing, calibration, sensor validation, closed-loop tuning, and first motion Integrate subsystems end to end — actuators, encoders, IMUs, tactile and force sensing, cameras, power distribution, comms buses (CAN/CAN-FD, EtherCAT, SPI/I2C, Ethernet), and onboard compute Own system-level behavior and interfaces: define and hold the mechanical, electrical, and software interface contracts between subsystems, and arbitrate the tradeoffs when they conflict Debug hard, ambiguous, cross-domain problems — intermittent faults, noise and grounding issues, timing and latency, thermal behavior, mechanical compliance masquerading as a control problem Write and modify embedded firmware and host-side tooling (C/C++, Python) for bring-up, calibration, diagnostics, and data capture Own the integration test plan: define what 'working' means per subsystem, automate the checks, and keep them passing as the design changes underneath them Characterize system performance against spec — accuracy, bandwidth, torque and thermal envelope, latency, power — and feed the gaps back to design as concrete requirements Build the data path: logging, telemetry, and analysis tooling that turns robot runs and test builds into duty-cycle, performance, and reliability evidence the team can design against Contribute to functional safety — including risk assessment, safety review participation, and validation of safe-state behavior Carry your own designs and integrations into a repeatable build: define the assembly and calibration sequence, the acceptance criteria at each stage, and the work instructions behind them Design, build, and deploy test stations and end-of-line test: fixturing, harnesses, DAQ, load application, station software, limits, and the data logging behind them Drive first-pass yield and cycle time on the builds you own: find the top failure contributors and close them with design, firmware, process, or fixture changes Establish calibration and traceability at scale — per-unit calibration data, serialization, and a data trail that supports later failure analysis and field investigations Bring DFM/DFA and DFT thinking into design reviews — testability, tolerance realism, and assembly practicality, pushed in before the design is locked Support NPI builds hands-on (EVT/DVT/PVT) and partner with CMs and suppliers on process transfer, station duplication, and incoming quality; travel to build sites as needed Partner with design ME/EE/FW, controls, quality, manufacturing engineering, and lab operations; document interfaces and processes so integration knowledge does not live in one person's head BS in Mechatronics, Electrical, Mechanical, Robotics, Manufacturing, or Computer Engineering, or equivalent 7+ years building, debugging, and transitioning real electromechanical systems from prototype into volume — systems that had to work outside a simulator and be built more than once Proficiency in C/C++ for embedded targets and Python for tooling, station software, and analysis AI-native working style: uses AI coding agents and LLM tooling as a default part of the job — to scaffold bring-up scripts, station software, and log-analysis pipelines, mine large run and build datasets for failure signatures, and draft work instructions, test plans, and interface documentation — while independently validating every AI-produced result against the hardware and the data before it drives a build or a design decision Hands-on electronics skills: reading schematics, bring-up of custom boards, soldering, harness fabrication, oscilloscope/logic-analyzer/DMM debugging Demonstrated ownership of a test station or automated test process you designed, deployed, and maintained for others to use Practical experience with at least one real-time comms bus (CAN/CAN-FD, EtherCAT, RS-485, SPI/I2C) and with motor drives or servo systems Working understanding of closed-loop control: PID, feedforward, current/velocity/position cascades, filtering, and why a tuned gain set stops working when the mechanics change Mechanical literacy: reading GD&T-annotated drawings, CAD (Creo, SolidWorks, or similar), tolerance stack-ups, fastener and preload basics Experience writing work instructions, test plans, and process documentation others actually follow Disciplined software habits — version control, code review, reproducible scripts 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 Experience in a fast prototype environment where you specified and procured your own hardware Manufacturing engineering depth: yield and Pareto analysis, SPC, process capability (Cp/Cpk), MSA/Gage R&R, PFMEA, control plans, 8D Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience bringing up humanoid, legged, or high-DOF robots, or high-performance actuators 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 Functional safety experience: ISO 13849 / ISO 10218 / IEC 61508, safety relays and STO, risk assessment authoring Motor control depth: FOC, commutation and encoder calibration, torque-constant and friction characterization Experience through a full NPI cycle (EVT → DVT → PVT → MP) on an electromechanical product, including CM or supplier process transfer EMC/EMI, grounding and shielding, and power-integrity troubleshooting on mobile platforms ROS 2 / real-time Linux, MCAP or similar log formats, and robot data infrastructure MES/manufacturing data systems, station data pipelines, and yield dashboards Test automation at scale — HIL rigs, automated regression on physical hardware, fleet dashboards

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Platform: meta
Effort: medium

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