Owns end-to-end sensor system performance for consumer wearables, from hardware selection and integration through firmware, signal processing, and ML integration. Requires 5+ years in systems engineering with expertise in DSP, embedded systems, and shipping high-volume products.
175k – 280k/yr
On-site5+ YOEEmbedded Engineering
About the role
Responsibilities
Sensing Architecture: Research, evaluate and recommend optimal sensor technologies and devices for various wearable applications, taking into account physical, electrical, and software capabilities along with cost, schedule, and user impact.
End-to-end Performance: Own the end-to-end performance of Sesame devices’ sensor systems from prototyping to mass production, including latency, power consumption, thermal constraints, and reliability.
System Requirements & Test: Champion a high quality user-experience by defining system-level test plans and acceptance criteria and detailed, actionable specifications for designing and validating each layer of the stack.
User data collection: Design and supervise a data collection strategy to gather ground-truth data sets necessary for algorithm and model development.
Algorithm Design: Develop, test, and implement the signal processing, sensor fusion and calibration systems that translate raw sensor data into usable outputs.
Model-sensor integration: Collaborate with Sesame’s ML team to determine how sensor data improves the quality of Sesame agents’ responses.
Required Qualifications
5+ years of experience in systems engineering, sensor systems, algorithms and/or signal processing.
Demonstrated experience shipping high-volume consumer products from concept through production, in collaboration with electrical, firmware, and mechanical engineers.
Background in Sensor Physics: deeply understanding how sensor systems interact with the physical world, and how to design a test plan to isolate specific variables and behaviors in the system.
Understanding of electro-mechanical integration requirements and limitations of various sensors: Implementation constraints, best practices, areas for optimization.
Strong understanding of embedded systems: SoC architectures, memory management, wireless connectivity, power and thermal management, latency.
Proficiency in Digital Signal Processing (DSP) techniques: filtering, windowing, feature extraction, and sensor fusion.
Expert in Python for signal processing, data visualization, and algorithm development.
Excellent communication skills and ability to drive alignment across multiple engineering disciplines.
BS/MS in Electrical Engineering, Mechanical Engineering, Physics, or Computer Science (or equivalent experience).
Preferred Qualifications
Experience with wearable or small-form-factor consumer devices, especially on-device voice, audio, or camera processing pipelines.
Familiarity with AI/ML workloads on edge devices and their system-level implications (compute, memory, power).
Hands-on experience porting of algorithms and models to embedded systems, including experience with C/C++.
Multiphysics / FEA Experience: Proficiency with simulation tools (e.g., COMSOL, ANSYS) to model mechanical-electrical coupling and sensor response.
Experience with Machine Learning for signal processing.
Direct experience defining system architecture for a 0-to-1 product.
Experience in a startup or fast-moving, small-team product environment.
Benefits
401(k) max employer match: 3.5% of compensation
100% employer-paid health, vision, and dental benefits for you and your dependents
Unlimited PTO and sick time
Flexible spending account with employer matching up to $1,650/year (medical FSA)
Guardian Employee Assistance Program (EAP)
Opportunity to share in the company's success with competitive stock options
Skills
PythonDigital Signal ProcessingSensor FusionEmbedded SystemsC/C++ComsolAnsysMachine LearningSignal ProcessingSoc Architectures
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