Project Engineer
- Market p25–p75
- $161k–$239k
- Noble Machines p25–p75
- $168k–$200kn=5
- H-1B median · Software engineering
- $190k
- Opened · 30d
- +0
- Closed · 30d
- −2
- Opened · 7d
- +2,0485% of open
- All software engineering
- 75,153open
Project Engineer
Sunnyvale, CA · Full-time · Travel ~15%
About Noble Machines
Noble Machines builds multipurpose robots to support human workers in the world’s toughest jobs, turning dangerous work from a necessity into a choice. Our work demands reliability, robustness, and readiness for the unexpected, on time, every time. We’re assembling a mission-driven team focused on real impact in heavy industry, from construction and mining to energy and logistics. If you’re driven to build rugged, reliable products that solve real-world problems, we’d love to talk.
About the Role
The Project Engineer manages the path from lab capability to repeatable, measured, on-site performance. You integrate Moby’s software and hardware for a customer’s environment, prove it with acceptance tests and evidence, and run the deployment end-to-end with the customer. You also take on special projects, e.g., executive and trade-show demos, international robot shipments and site bring-up, customer training cohorts, and new test infrastructure – cross-functional efforts for which you author the playbook and procedures, architect and improve the process toward scalable standard operating procedures. This is a hands-on, high-ownership role for someone equally at ease building an integrated schedule, debugging a robot on a job site, and briefing a customer’s operations lead.
What You’ll Do
Deployments
- Site readiness: Survey customer sites (environment, lighting, floor and door geometry, network, power, infrastructure). Create the site survey report, listing the gaps to close and the requirements for go-live.
- Integration: Stage acceptance tests in Noble Machines’ lab under conditions that mirror the customer site, covering navigation, perception, and manipulation tasks.
- Test and evidence: Write a test plan for each milestone, run trials, log every run, and produce the evidence pack (metrics, video, fault log).
- Sim-to-real: Feed site data (maps, lighting captures, geometry) back to engineering, and validate fixes in simulation before field rollout.
- Power characterization: Build the duty-cycle and charging model for each deployment, and define on-site charging locations and schedules.
- Safety: Run on-site risk assessments, e-stop procedures, and operator training. Enforce exclusion zones during trials.
- Site operations: Work with the customer’s site team on readiness of tools and equipment (PPE, KVM carts, workbenches, crash carts, site safety training).
- Data outputs: Set up logging and a customer-facing KPI view, and train customer staff to read it.
- Field support: Triage hardware, firmware, and software faults, and lead root-cause analysis. Coordinate spares and RMAs with operations.
- Training: Deliver hands-on training to customer operators and site personnel on safe, effective robot operation.
Special projects
- Own time-boxed, cross-functional projects end-to-end: executive and trade-show demo builds, international shipments and post-transit bring-up (re-calibration, local power, network), customer training cohorts, test-bench and gantry setups, and new-variant pilots.
- For each project, define scope, success criteria, schedule, and owners. Close it with a short retrospective and a reusable playbook.
Schedule and cross-functional leadership
- Lead without direct authority across navigation, perception, manipulation, and controls engineering; hardware and field operations (spares, shipping, RMAs); safety/EHS; and the customer’s operations team.
- Run the cadence: daily stand-up during ramp-up, a weekly technical sync with the customer, and a weekly status and risk report to the TPM.
- Hold a RACI for each milestone. Manage the critical path and dependencies. Escalate blockers within 12 h, and any slip over 2 days within 24 h with a recovery plan.
- Make or drive scope, test, and schedule trade-offs, and document each decision.
- Turn field learnings, edge cases, and customer feedback into concrete product requirements. Build scalable deployment processes, tooling, and documentation as the customer base grows.