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San Francisco, California, United States
Onsite
Not Available
We are seeking a creative, hands-on Senior Research Engineer to lead ambiguous sensing and measurement problems with real-world impact.
You will become an expert in how our grid sensor signals behave in the real world. You will investigate sensing issues and performance via exploratory data analysis, hardware simulation, and bench debugging, design improvements, and validate improvements using test infrastructure you develop. You will define sensing requirements, develop measurement-chain improvements, and help mature new sensing capabilities.
This position focuses on measurement performance, validation, and technology transfer rather than product design and implementation.
Develop measurement performance requirements. Select and evaluate new sensors for current and new sensing capabilities.
Root-cause sensor signal issues from fleet to bench: use fleet telemetry (time-series + metadata) to isolate cohorts and failure signatures, test hypotheses via circuit simulation and benchtop reproduction, drive fixes design solutions, and partner with HW and FW engineering to implement them.
Design and validate measurement chain changes to improve physical phenomena observability.
Downsize on-device data. Develop signal compression techniques, selective data storage decisions, and sampling rate reduction strategies.
Develop and own test methods to characterize and validate sensor performance.
Develop hardware-in-the-loop test infrastructure to reproduce the real-world physical phenomena Gridware technology detects. Run hardware-in-the-loop tests to validate changes to our tech stack (HW, phenomena detection algorithms).
Mentor team members. Raise the technical rigor of experiments, analysis, and validation work.
Collaborate closely with product managers, data scientists, and SW/HW/FW engineers.
MS in Electrical Engineering, Physics, or a closely related field plus 5+ years of relevant industry experience owning ambiguous sensing / signal-quality problems end-to-end, or PhD in one of those fields plus 2+ years of relevant industry experience.
Strong fundamentals in electromagnetics.
Track record of owning ambiguous sensing, signal-quality, or measurement-performance problems from framing through validation.
Scientific computing: comfortable writing analysis pipelines in Python, MATLAB, or equivalent to investigate and report system performance.
Experience collecting high-quality electrical measurements: have built and run custom measurement setups using standard lab instrumentation (scope/DAQ and related tools).
Strong technical judgment, communication, and cross-functional collaboration.
Experience leading technically complex work with multiple stakeholders and deadlines.
We do not expect every senior candidate to be equally deep in every area below. Strong candidates will usually bring deep experience in several of these areas.
Sensor characterization and validation: led sensor performance evaluation from characterization, through test method development. Experience developing performance requirements and testing or validating sensing systems against those requirements.
DSP in embedded systems: have applied DSP to real sensor signals in embedded systems. Competent in C/C++ (or similar low-level languages), timing, sampling, and sensor communication interfaces.
Circuit simulation: have used SPICE-class simulation (LTspice/PSpice/ngspice/Spectre or similar) to model HW and perform investigations.
Root-cause sensor signal issues: have a track record investigating and solving signal/noise issues, e.g. identifying issues through exploratory data analysis, reproducing issues in simulation and on the bench, and implementing solutions.
Physical sensing research: have researched new physical sensing capabilities using first-principles modeling, experiments, and sensor trade studies to evaluate feasibility and performance.
Design and automate validation: have designed system validation plans with explicit acceptance criteria. Have built or owned repeatable test infrastructure.
Experience with deployed IoT fleets (tens of thousands of devices) and developing “observability for sensor performance” e.g. telemetry design, health metrics, calibration drift monitoring.
Experience optimizing sampling and signal processing on constrained compute devices to reduce power and storage.