Physical Systems
Engineers, technicians, and operators working on hardware design, robotics, embedded systems, manufacturing, and safety-critical physical systems. Covers chip/silicon design, mechanical and electrical engineering, firmware/embedded software, robotics operations, systems safety, physical manufacturing, and datacenter hardware installation and cabling.
Roles
The canonical roles within Physical Systems.
Systems Engineer (Hardware)
Systems Engineers in this slug lead system-level integration of hardware, software, and mechanical subsystems for physical products and infrastructure—across two broad clusters. The first is AI-powered physical products: autonomous vehicles, robotics, satellites, and defense systems, where the role spans architecture, requirements, and verification across cross-functional engineering teams. The second is large-scale physical computing infrastructure: AI data centers and modular compute systems, where the role centers on integrating mechanical, electrical, and thermal subsystems and leading commissioning and validation activities through deployment. Both clusters share the underlying craft of system-level integration, requirements management, and cross-domain coordination. These engineers typically sit within hardware, systems, or infrastructure engineering organizations, working alongside domain specialists in mechanical, electrical, software, and operations functions.
Hardware & Electrical Engineer
Engineers in this role design and optimize hardware systems powering AI compute platforms, from portable multimodal devices to data center infrastructure. Their work spans power architecture and battery management for edge AI products, signal and power integrity for complex PCBs handling high-speed compute signals, and hands-on validation of AI accelerators, cooling systems, and electrical distribution at scale. These roles sit at the intersection of product development and infrastructure, working closely with firmware, systems, and manufacturing teams to ensure AI hardware performs reliably under demanding workloads while meeting thermal, electrical, and safety constraints.
Mechanical & Thermal Engineer
Engineers in this role design and optimize mechanical systems, thermal management solutions, and structural components for AI hardware infrastructure and advanced autonomous systems. They work hands-on across the full product lifecycle—from concept and CAD modeling through testing and production—solving complex engineering challenges like cooling strategies for high-density compute clusters, structural integrity under extreme operating conditions, and integration of electronics into ruggedized platforms. These roles typically sit within specialized engineering teams at AI and defense-focused companies, collaborating closely with thermal, electrical, and systems engineers to deliver mission-critical hardware that meets demanding performance and reliability standards.
Manufacturing & Production Engineer
Engineers in this role guide the journey of AI hardware—from prototype to mass production—designing and optimizing manufacturing processes for PCBs, assemblies, and mechatronic systems. They partner with design teams, contract manufacturers, and cross-functional stakeholders to solve complex manufacturability challenges, conducting detailed design-for-manufacturing reviews and managing new product introductions while scaling production efficiency and quality. Typically embedded within hardware or operations teams at AI companies building inference systems, robots, or autonomous vehicles, they balance technical rigor with hands-on problem-solving, translating engineering intent into reliable, repeatable factory processes.
Industrial Technician
Industrial technicians in this role diagnose, repair, and refurbish hardware units returned from the field, turning each repair into actionable failure data that feeds product reliability cycles. They combine hands-on electronics repair—soldering, component swaps, reassembly—with meticulous documentation practices, treating repair logs as critical data inputs rather than administrative tasks. These roles sit within manufacturing and reliability teams at AI hardware companies, working closely with engineering to identify failure patterns that improve future device generations and accelerate production timelines.
Chip & Silicon Engineer
Chip & Silicon Engineers at AI companies work across the chip-design lifecycle for AI accelerators and supporting silicon—from RTL and microarchitecture through physical design, verification, and post-silicon validation. The role spans front-end design (architecting blocks, writing RTL, running simulation), physical design (synthesis, place-and-route, timing closure, power-performance-area optimization), and post-silicon work (bring-up, characterization, debug across hardware, firmware, and software layers). Specialization within this slug varies—some engineers focus narrowly on one phase of the pipeline, others coordinate across phases—but the population spans the full chain rather than concentrating on any single stage. These engineers typically sit within silicon, hardware, or platform engineering organizations at chip-focused AI companies, collaborating closely with verification, software, and systems teams to deliver production silicon.
Datacenter Field Technician
Datacenter Field Technicians lead on-site execution of infrastructure deployments for AI cloud platforms, directing crews across mechanical, electrical, cabling, and hardware installation while managing quality, safety, and vendor coordination. They distinguish themselves through project leadership and real-time troubleshooting in high-stakes buildouts, rather than routine maintenance alone, ensuring that complex systems meet specification before energization. These technicians sit within data center operations or project delivery teams at hyperscale AI companies like CoreWeave, Nebius, and SpaceXAI, where they serve as the technical authority connecting construction trades, engineering, and ops to accelerate AI compute capacity.
Embedded & Firmware Engineer
Engineers in this role develop and optimize firmware that powers AI infrastructure hardware—from baseboard management controllers in data centers to motor controllers in robotics systems to camera sensor drivers in vision platforms. They work at the boundary between silicon and software, writing low-level C/C++ code to manage power, thermal systems, sensors, and real-time control, often using RTOS environments and debugging with JTAG and oscilloscopes. This work distinguishes itself from higher-level embedded software engineering by its focus on board bring-up, hardware validation, and tight hardware-firmware integration during product bringup. These engineers typically sit in hardware-adjacent teams within AI companies—working closely with silicon teams, hardware engineers, and systems architects to ensure new AI chips and platforms function reliably at scale in production environments.
Robot Operator
Robot operators at AI robotics companies spend their days running humanoid or autonomous robots through real-world tasks, identifying malfunctions, and relaying technical feedback to engineering teams through structured issue tracking. What distinguishes this work from general equipment operation is the emphasis on data quality, teleoperation coordination, and close collaboration with AI training pipelines—operators are not just managing hardware but actively generating the observations that improve robot learning and autonomy. These roles sit within operations or deployment teams at companies building general-purpose robotics, working shifts that span 24/7 coverage and often requiring physical coordination, sustained focus, and the ability to troubleshoot mechanical and electrical issues under tight timelines.
Robotics Engineer
Robotics engineers in this role own the full stack of physical robot systems—from mechanical design and actuator integration through perception, control algorithms, and autonomous navigation. They spend their days building and debugging real hardware, implementing motion planning and state estimation, integrating sensors, and closing the simulation-to-reality gap through iterative testing and validation. What sets this role apart is the hands-on responsibility for end-to-end system performance: these engineers don't just write algorithms, they physically integrate them onto platforms, troubleshoot discrepancies between simulation and real behavior, and iterate rapidly in response to field testing. Robotics engineers typically sit within dedicated platform or autonomy teams at AI robotics companies—alongside firmware engineers, mechanical engineers, and autonomy software teams—working on systems like humanoid robots, unmanned aerial vehicles, industrial manipulators, or mobile platforms where perception and control directly impact real-world task execution.
Vehicle Operator
Vehicle operators at autonomous vehicle and robotics companies execute core mission functions—whether piloting autonomous aircraft in deployed environments, operating test vehicles on public roads and tracks, or managing fleet recovery operations at scale. What distinguishes these roles is their direct responsibility for generating real-world performance data that feeds back into AI model improvement, while maintaining rigorous safety and compliance standards in high-consequence settings. They typically embed within specialized operations teams reporting to heads of fleet, deployment, or market operations, working cross-functionally with engineering and customer-facing leadership to translate field insights into product refinements.
Systems Safety Engineer
Systems Safety Engineers at autonomous vehicle and robotics companies conduct comprehensive hazard analyses, risk assessments, and functional safety evaluations to ensure AI-driven systems operate safely in real-world environments. They lead cross-functional efforts to define safety requirements, develop mitigation strategies, and verify that implemented controls effectively reduce risk across hardware, software, and operational domains. What distinguishes this role is its focus on safety-critical AI systems where failures can directly impact human safety, requiring deep engagement with international standards like ISO 26262 and continuous validation against field data. These engineers typically embed within dedicated safety teams, working alongside product, engineering, and regulatory stakeholders to navigate novel safety challenges in emerging autonomous technologies.
Recent Jobs
The latest Physical Systems openings across the AI industry.