Systems Integration Engineer, Embedded Systems / Mechatronics

Meta•Redmond, WA

1d•$173,000 - $245,000

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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