Convert sensor algorithms and build ML inference pipelines into efficient embedded C/C++ code for microcontrollers or other constrained platforms.
Optimize code for memory footprint, CPU usage, and real-time performance.
Co-develop with algorithm / ML researchers to refine models for embedded deployment.
Profile runtime behavior, identify bottlenecks, and perform low-level debugging.
Work with firmware teams to integrate sensor algorithms / ML models into system software.
Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.

Requirements

BS/MS in Electrical Engineering, Computer Engineering, Computer Science, or related field.
Strong proficiency in C/C++ for embedded systems.
Ability to read/translate algorithmic descriptions in Python into low-level codes.
Experience translating and optimizing machine learning models for embedded targets (e.g., quantization, fixed-point, pruning).
Understanding basic DSP concepts (filters, FFTs, spectral processing, etc.)
2+ years of experience pushing sensor algorithm or ML models to production (C++)
Solid software engineering skills and proficiency in Python

Benefits

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
Paid parental leave
Alternating day off (every other Monday)
“Off the Grid”, a two week per year paid break for all employees.
Commuter allowance
Company-paid training

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

CC++embedded systemsmachine learningalgorithm optimizationlow-level debuggingDSP conceptsPythonquantizationfixed-point

Certifications

BS in Electrical EngineeringMS in Electrical EngineeringBS in Computer EngineeringMS in Computer EngineeringBS in Computer ScienceMS in Computer Science