Sr. Staff Embedded AI Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Not verified checked Jun 7, 2026

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

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Seniority

Senior From the posting source

Schedule

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Weekend work

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Deadline

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About this role

Sr. Staff Embedded AI Engineer Columbia, MARYLAND, United States Company Description: Renesas is seeking a Sr. Staff Embedded AI Engineer to develop advanced TinyML and embedded AI solutions targeting Renesas microcontroller and MPU platforms (RA, RL78, RX, RZ). This is a highly technical, hands-on role focused on building cloud-based model translation infrastructure and optimizing network inference for resource-constrained embedded systems. You will contribute to a small team developing a service that converts trained machine learning models into efficient C/C++ implementations for deployment on microcontrollers. The ideal candidate combines strong embedded software expertise with solid machine learning fundamentals and is comfortable working across the stack - from neural network internals to low-level performance optimization. You should be someone who contributes new ideas, challenges assumptions, and helps improve both tooling and embedded implementation quality Job Description: BS/MS/PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field. 6+ years of experience in embedded systems software development. Strong proficiency in C/C++ for embedded platforms. Strong proficiency in Python for tooling, automation, or ML workflows. Experience deploying machine learning models to resource-constrained systems. Solid understanding of neural network fundamentals and internals Experience with machine learning frameworks such as TensorFlow or PyTorch. Experience optimizing performance, memory footprint, and power consumption on embedded targets. Qualifications: • Experience developing inference runtimes, model translation tools, or code generation systems. • Experience with CMSIS-NN or other embedded ML acceleration libraries. • Experience optimizing quantized neural networks for embedded systems using SIMD/DSP acceleration. • Familiarity with Renesas MCU/MPU platforms (RA,

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