Mickael Renault — Sensing Firmware & Algorithms Engineer

Sensor firmware and algorithms engineer with 10+ years building real-time, embedded sensing systems across consumer electronics: sports/fitness, wearables/audio, and smart home products. Track record of taking products from early-stage R&D through mass-production validation, at every scale: a venture-backed startup, a mid-size consumer-hardware company, and Apple. Combines hands-on depth in algorithm design and firmware architecture with real people leadership, including a 10-engineer distributed team led from alpha through launch. Open to a Team Lead / Manager role in sensing firmware and algorithms. Motivated, proactive, and thriving where deep technical problems meet cross-functional teams.

Core Expertise

Sensing & Algorithms: Sensor fusion, real-time calibration, motion detection & tracking, predictive/physics-based modeling, signal processing, 3D simulation & visualization
Firmware: Embedded architecture (ARM Cortex-M0/M4, embedded Linux), driver development, OTA update, factory diagnostics & validation, low-power design
Leadership: Team leadership & mentoring, cross-functional technical negotiation, end-to-end ownership (DRI), knowledge-management systems, onboarding & process design
Languages/Tools: C, C++, Python; Git, Jupyter; AI-assisted tooling & architecture

Selected Recent Work (2025–2026)

Sensing algorithm design & cross-team validation infrastructure

Designed a lightweight real-time algorithm that delivered a new context-detection capability for a wireless-audio sensing feature from a sensor already on the device, avoiding additional hardware, power, and cost. To validate it at scale, partnered with the QA/Automation team to co-design the instrumentation interface for automated testing, shifting the team's role from handing off algorithms for validation to building the validation infrastructure itself.

Cross-functional 3D sensor visualization & simulation tool

Built a frame-by-frame 3D replay engine (Python + Blender) for sensor failure analysis, featuring physics-based volumetric sensor error modeling and an embedded-feasible processing pipeline. Resolved cross-team friction by giving engineers a shared, unbiased view of the sensing system, used across teams from day-to-day technical collaboration to leadership review.

AI-optimized internal knowledge platform

Identified a systemic gap — teams restarting from scratch on each new program with little knowledge transfer — and built a full internal engineering playbook and issue-retrospective platform, deployed as a live internal web service. Designed to be AI-agent-navigable from day one with LLM entry point, search index, and manifests.

Independent product R&D

Led an independent, self-directed R&D effort evaluating a new health-sensing product concept end-to-end: competitive/market analysis, UX research, and functional hardware prototyping.

Professional Experience

2019 — Present

Apple Inc. — Sensor Firmware & Algorithms Engineer

Sep 2019 – Present · Los Angeles, CA

Sensing Platform Bring-up & Technical Leadership (2023–2026)

Technical lead across two sensing programs: owned architecture decisions, represented engineering in cross-functional design reviews, and surfaced/unblocked risks early.
Kick-started sensing firmware architecture and implementation for a new product program ahead of team scale-up; delivered firmware for prototyping platforms across multiple leadership demo cycles.
Led multi-sensor array design through extensive simulation efforts (Python, Blender), converging on the final production configuration.
Delivered initial algorithms for motion compensation and spatial awareness.
Served as the primary owner (DRI) for Sensing Firmware across two consecutive programs: factory firmware diagnostics, calibration flows, and validation. On one, resolved a compliance constraint by migrating the codebase from C++ to C, re-validated end-to-end with no schedule slip.
Became the sensing platform's cross-functional source of truth: architecture diagrams, configuration definitions, and validation procedures relied on by multiple teams daily.
Built the knowledge and process foundation that scaled two programs: 15+ engineers onboarded, plus issue-tracking and sprint-planning standards adopted team-wide.
Mentored summer interns through user study, algorithm design and performance analysis, and cross-functional review of the results.

Ambient Sensing R&D — Sensor Hub Prototyping & Home Climate Algorithms (2021–2023)

Led design and development of two generations of sensor-hub research prototypes for controlled in-home data collection, integrating HomeKit bridging and multi-modal environmental and presence sensing to support home-climate and comfort algorithm research.
Built an end-to-end data collection pipeline and backend with real-time web visualization, supporting multi-user/multi-device studies.
Designed room-level temperature characterization and predictive algorithms to forecast indoor thermal behavior; built a real-time analysis server with interactive projections and visualizations.
Built iOS apps in Swift for real-time sensor monitoring and comfort analytics; ran a user comfort study integrating wearable devices and skin-temperature sensing.
Led cross-functional collaboration with platform software, iOS sensing, and energy teams for demos and technical alignment.

Smart Home Speaker — Ambient Sensing (2019–2021)

Owned ambient light and environmental sensor placement, calibration, firmware drivers, and algorithms on an embedded platform, including a device self-heating temperature compensation algorithm.
Enhanced the user experience through new sensing capabilities: environment- and context-aware features, control loops, and capacitive touch integration for on-device controls.

2018 — 2019

Simplehuman LLC — Embedded Software Engineer

Dec 2018 – Sep 2019 · Torrance, CA

Simplehuman Sensor Mirror Hi-Fi — Sensor Mirror Hi-Fi

Shipped production firmware for the Sensor Mirror Hi-Fi (Cortex-M0): ALS and capacitive-touch drivers, calibration, ESD recovery, and light-control algorithms.
Implemented robust application-level OTA updates (dual-bank method), enabling safe in-field firmware upgrades.
Developed the calibration model for the mixed-LED lighting system, delivering accurate, consistent light output across units.

2016 — 2018

GameGolf, LLC — Team Lead & Embedded Software Engineer

May 2016 – Dec 2018 · San Francisco, CA

GameGolf engineering team — Engineering team, GameGolf

Connected wearable for golf players: led new-generation product development of a low-power embedded system, from alpha through launch.

Led a team of 10 engineers, coordinating distributed teams across California, Ireland, and Ukraine; reported directly to VP and CEO.
Owned roadmap prioritization, task decomposition, feedback cycles, and documentation across engineering and executive stakeholders.
Designed firmware architecture for low-power CPUs (Cortex-M4, Cortex-M0) and real-time on-device algorithms: swing detection, activity/power management, on-the-fly 9-axis IMU calibration, embedded sensor fusion.
Designed golf-specific motion models in MATLAB; filed 2 provisional US patents (motion detection, battery management).
Built a machine learning back-end pipeline (genetic algorithm, C/Python) for swing detection, with feature propagation to the embedded system over BLE.

Early Career — Embedded Systems, R&D & Entrepreneurship

2014 — 2016

SportSense — Project Lead & Embedded Systems Engineer

Sep 2014 – Apr 2016 · Antibes, France · Part-time

Self-initiated entrepreneurship project incubated at the University of Nice: full end-to-end hardware/firmware/software design for a sensor network for the national gymnastics training center (ToF sensors, PCB design, Bluetooth, embedded Linux); the project earned the University of Nice Foundation's Entrepreneurship Award.

2014 — 2015

CNRS GeoAzur — Firmware Developer, Professional Seismometer

Dec 2014 – Jun 2015 · Sophia Antipolis, France · Part-time

End-to-end development: Python DSP and seismic data analysis, real-time visualization UI, SEED-compliant data storage backend.

Education

2016

Master of Sciences in Electrical Engineering — Embedded Systems

University of Nice Sophia Antipolis, France

2015

Certificate in Small Business Management & Entrepreneurship

University of Nice Sophia Antipolis, France

Skills

Programming: C, C++, Python
Embedded Platforms: ARM Cortex-M0/M4, embedded Linux, Raspberry Pi, Bluetooth/BLE SoCs
Sensors: Ambient light (ALS), 9-axis IMU, ToF (depth/ranging), passive-infrared (PIR) presence, temperature/humidity, air quality (CO₂, PM2.5), capacitive touch, in-ear/proximity detection, ultrasonic
Algorithms: Sensor fusion, motion detection & tracking, real-time calibration, predictive modeling, signal processing, genetic algorithms/ML, physics-based sensor error modeling, 3D visualization (Blender)
Firmware & Hardware Bring-up: Driver development, OTA update, factory diagnostics & validation, ESD & error recovery, low-power architecture, BLE integration, 3D modeling & printing for test fixtures and prototyping
Software & Data: Real-time sensor data pipelines (ingest → SQL aggregation → remote visualization) for large-scale studies, web backends, HomeKit integration, iOS app development
Leadership & Process: Team leadership, mentoring, cross-functional negotiation, end-to-end ownership (DRI), knowledge-management systems, AI-accessible documentation design
Languages: English (fluent), French (native)

Patents & Awards

2019

Provisional US Patent AN 62/778,654

Electronic tag for shot detection

2016

Provisional US Patent AN 62/557,225

Motion and gesture analysis from a magnetic and inertial measurement unit

2015

Entrepreneurship Award

University of Nice Sophia Antipolis Foundation

2015

Junior Project Award

STMicroelectronics, E-Same Contest

2009–2015

Elite Athlete, French National Team

Trampoline

1st, National Cup, 2014
8th, World Cup, Arosa, Switzerland, 2014
1st, National University Championship, 2013
21st, World Championship, Sofia, Bulgaria, 2013