Sr. Staff Engineer

Sr. Staff Engineer

Role Overview

The Peripheral & Variant Lead is the technical leader for peripheral blocksets and

device variant scaling within Renesas Hardware Support Packages. This role

delivers high‑quality Simulink peripheral blocks (e.g., ADC, PWM, UART, CAN) using

the foundation layer, and drives scalable enablement across MCU variants through

automation, validation, and standardized architecture. The role ensures alignment

with core dependencies and works closely with the foundation team to deliver

production‑ready solutions.

Key Responsibilities

Lead development of peripheral blocksets (ADC, PWM, UART, CAN, etc.)

leveraging foundation components and established integration patterns.

Drive device variant augmentation, defining validation strategies and

automation frameworks to scale across Renesas MCU families

Define block architecture, coding standards, and integration patterns to

ensure consistency, reusability, and long‑term maintainability.

Review IO Builder / System Object implementations and optimize for

performance, usability, and customer experience.

Collaborate closely with the MW foundation team to maintain alignment with

core dependencies and release compatibility.

Qualifications

10+ years in embedded systems with strong experience in peripheral driver

development.

Demonstrated experience building support packages / peripheral enablement

frameworks at a semiconductor supplier or tools ecosystem team.

Strong experience with MATLAB/Simulink and MBD concepts, including

reusable modeling patterns and integration with code generation.

Proven ability to design scalable architectures for device variant support,

automation, and validation/regression testing.

Senior Staff‑level technical leadership: architecture ownership, rigorous

reviews, mentoring, and cross‑team influence.

Added Advantage

Experience creating custom blockset packages / peripheral libraries /

MCAL / CDD for semiconductor solutions.

Familiarity with IO Builder/System Object patterns and performance tuning

in model‑based workflows.