Work with Andrew
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Embedded & connected devices
Production devices in the field, by a computer engineer whose first language was C: Del Mar Vacations' AWS IoT digital twins deployed across a vacation-rental fleet in mid-expansion (227 managed homes in Nov 2020, grown to 320 by late 2021), integrating a sprawling, heterogeneous device estate: Flo, Flume, YoLink LoRa, SensorPush, Minut, Tank Utility, and a mixed installed base of locks, alarms, thermostats, and networking gear, among others, plus custom ESP32 pressure nodes and Raspberry Pi gateways Andrew built at the bench and field-installed alongside the crews, with a React Native field-service app for the crews acting on the data; and MMD Inspector's mold-sensing workflow: Protimeter BLE meters and FLIR thermal integrated, custom leave-behind sensors taken through contract manufacturing to a mass-production build. Startup device work, stated exactly: Bevi's Standup dispenser (2016–18): on-machine screen/UI programming, telemetry/backend integration, and in-field machine servicing; Pavlok's post-launch hardware revisions (2015–17): hands-on across most of the module (stimulus circuit, vibration motor, accelerometer, BLE SoC, battery/charging) plus companion software; WOO's kiteboarding jump sensor (Kinematiq, 2015–16): session software and spec-level work on the 9-axis IMU + BLE stack; ROCKI's second-generation Wi-Fi audio streamer (2014–15): software with component-spec involvement (Wi-Fi module, DAC/audio path, control protocol); and contract work out of MakerBot's Newbury Street offices in the Replicator era (2014–15). Manufacturing responsibility at the managing/interfacing level: sourcing, component specs, and production communication with Chinese manufacturers across several device engagements, plus MMD's mass-produced, contract-manufactured sensors (2020–21). Recent custom builds (2024–2026), in real third-party use, cover the core device patterns of sensing, OTA updates, companion-app communication, and safety interlocks: a remote-start safety interlock on a heating element built to the UL 60335 standard, an ESP32 water-pressure/leak node with OTA updates, a BLE environmental probe with a companion app, and a thermal-protection phone enclosure for sauna use.
Credentials
- Del Mar's home monitoring: an AWS IoT digital twin of every property, across a fleet grown from 227 to 320 homes
- Custom ESP32 sensor nodes built at his own bench; Raspberry Pi gateways where no product fit
- MMD's leave-behind sensors taken through contract manufacturing to a mass-production build
Why these lanes belong together
The same pattern keeps returning in my work: someone has a system nobody wants to take seriously, and it turns out to be load-bearing. Vacation-home water heaters; a mold inspector's leave-behind sensors; forty federal datasets; surgical training in hospitals with bad connectivity. Every home in a 320-property rental fleet got a digital twin. The mold sensor went through contract manufacturing. The datasets became APIs that AI agents query directly. The surgical platform's prize was $700,000. It runs in Ethiopia. That is the useful part.
Good fit
- Ambiguous zero-to-one systems where the technical direction and the implementation are the same job.
- Data-heavy AI products that must operate reliably in production, under real compliance constraints.
- Problems that cross boundaries: software to hardware, engineering to institutions, prototype to operations.
- Small teams that need a lead who ships alongside them.
Not a fit
- Pure research roles judged on publications. The AI-mechanism work is applied engineering, not an academic track.
- Roles that are exclusively people management with no hands-on technical scope.
- Teams seeking a narrow specialist in a single framework. The value here concentrates where problems cross domains.
- On-site-only roles outside Austin, TX.
A useful first email says what you're building, where it hurts, and what form of help you think you need. Specifics get the fastest reply. hello@andrewfribush.com. The full record is at the engagement ledger, the short version at the résumé. Machine-readable: /career/brief.json · /availability.json.