Prophet AI
Hardware Systems Engineer — Vision, RFID & Edge Compute
Prophet AI, Boston, Massachusetts, us, 02298
Overview
Location:
Boston, MA (Remote Possible)
Help us build a scalable, ruggedized sensor platform that fuses hundreds of cameras with RFID, temperature, and weight sensors to generate high‑resolution health and welfare insights in poultry production environments.
The Company The Company: Prophet AI
In the U.S. alone, poultry mortality results in over
$4 billion in annual losses
and
8 million metric tons (MMT) of CO₂-equivalent waste , driven by undetected disease, poor welfare, and suboptimal management. Prophet AI is an early-stage AgTech company building high-resolution poultry health and welfare monitoring to eliminate this waste. We fuse large-scale video, RFID-based identity, environmental sensors (temperature, humidity), and weigh scales, to run real-time computer-vision inference on edge GPUs. We partner with breeding and animal-health companies and commercial producers to turn sensor streams into actionable, individual-level insights that improve genetics, welfare, and operational efficiency.
The Role We’re hiring a hardware systems engineer to lead the design and productization of an end‑to‑end edge platform that can reliably ingest
several hundred cameras
and
100 additional sensors
(UHF RFID readers, temperature sensors, weight scales, etc.), while running real‑time computer vision algorithms on high-performance edge compute and syncing clean, time‑aligned data to the cloud. You’ll own architecture, vendor selection, prototyping, testing, and manufacturing hand‑off.
What You’ll Do
Own the architecture
for a multi‑sensor edge system (rack/cluster switching storage power) that scales from single‑site pilots to multi‑site deployments.
Camera ingest @ scale : Design low-res camera networks (640x480p) for hundreds of streams; PoE/PoE budgeting; VLAN segmentation; IGMP snooping/multicast; clock sync (PTP/NTP); health monitoring and auto‑recovery.
RFID at scale : Specify and integrate ~100 UHF RFID readers/antennas; dense‑reader‑mode planning; anti‑collision tuning; shielding and interference mitigation; LLRP middleware; tag/antenna mapping and calibration.
Aux sensors : Integrate temperature, humidity, and environmental sensors and weigh scales with timestamping and calibration workflows.
Edge compute & CV pipelines : Size and tune GPU nodes; PCIe/NVLink/GPU‑direct considerations; containerized deployment; DeepStream/GStreamer pipelines; NVDEC/NVENC throughput planning.
Storage & data management : Architect local NVR‑style retention (e.g., 30–60 days) with RAID/NAS/DAS; plan write/read IOPS and sustained throughput; implement object storage gateways and offline‑first sync.
Power, thermal, reliability : Size PDUs/UPS/generators; 208/240 V distribution; rack layout, airflow, and thermal characterization; EMI/EMC and surge protection; observability (Prometheus/Grafana) and alerting.
Security & fleet ops : Network segmentation (802.1X, VLANs), certificates/PKI, secure boot, SBOM tracking; provisioning with Ansible/Terraform; OTA updates and device health.
Productization : DFM/DFT, BOM ownership and cost‑down, enclosure/industrial design, compliance (FCC/UL/CE as applicable), manufacturing partner coordination, pilot → scale playbooks.
Documentation & leadership : Write clear architecture docs, SOPs, and test plans; mentor junior engineers; collaborate with CV/ML, software, and field operations.
Minimum Qualifications
Experience building
production
multi‑sensor or video systems in industrial/retail/logistics, robotics, autonomous systems, or security/NVR at 100 stream scale.
Deep experience with
camera systems ,
GStreamer/DeepStream ,
CUDA , and GPU resource planning (NVDEC/NVENC, memory bandwidth, PCIe lanes, NUMA).
Hands‑on with
UHF RFID
readers (e.g., Impinj/ThingMagic), antenna planning, LLRP integrations, and dense‑reader deployments.
Strong
networking
chops: L2/L3 switching, PoE budgeting, 10/25/40/100 GbE links, QoS, multicast/IGMP, PTP/NTP time sync, and site‑to‑cloud backhaul.
Proficiency in
Linux
(driver bring‑up, udev, systemd),
Docker , scripting ( Python/Bash ), and at least one systems language ( C/C
or
Rust ).
Experience integrating
industrial sensors
and weigh scales; calibration/verification procedures.
Proven ability to take hardware from
architecture → prototype → pilot → manufacturing , with vendor selection and
BOM cost
ownership.
Nice to Have
Experience with
Triton Inference Server , Kafka/MQTT, ROS 2, or time‑series databases.
PCB/schematic literacy and lab skills (oscilloscope, spectrum analyzer); EMI/EMC troubleshooting.
Field‑hardening in harsh environments (dust, vibration, corrosive atmospheres) and IP‑rated enclosures.
Observability: Prometheus/Grafana, Loki, OpenTelemetry; remote fleet management.
Domain exposure to animal/AgTech, food production, or other regulated industrial settings.
What Success Looks Like (0–90 Days)
30 days : Detailed architecture & bill of materials for a 100 camera / ~100‑reader system; risk register and test plan.
60 days : Lab demo with ≥64 cameras & ≥20 RFID readers; validated ingest and CV throughput; PTP sync working across devices; storage and backhaul benchmarks.
90 days : Pilot‑ready reference rack with scripts/automation, monitoring dashboards, and install guides; factory acceptance test (FAT) checklist complete
Pragmatic, test‑driven engineering with measurable throughput/latency/reliability targets.
Field‑first: designs must be installable, serviceable, and cost‑effective.
We value clear writing, generous collaboration, and bias‑to‑prototype.
Compensation & Benefits Competitive salary equity; equipment budget; travel.
Interview Process
1) 30‑min intro screen
2) 60‑min technical deep dive (CV/ML pipelines networking RFID)
3) 90‑min system design exercise
4) Panel with team
5) References
Application Send a short note, résumé/LinkedIn, and a
1‑page case study
of your largest multi‑sensor deployment (camera/RFID counts, throughput, and lessons learned) to
jean@prophetai.io
with subject
“Hardware Systems Engineer”
#J-18808-Ljbffr
Boston, MA (Remote Possible)
Help us build a scalable, ruggedized sensor platform that fuses hundreds of cameras with RFID, temperature, and weight sensors to generate high‑resolution health and welfare insights in poultry production environments.
The Company The Company: Prophet AI
In the U.S. alone, poultry mortality results in over
$4 billion in annual losses
and
8 million metric tons (MMT) of CO₂-equivalent waste , driven by undetected disease, poor welfare, and suboptimal management. Prophet AI is an early-stage AgTech company building high-resolution poultry health and welfare monitoring to eliminate this waste. We fuse large-scale video, RFID-based identity, environmental sensors (temperature, humidity), and weigh scales, to run real-time computer-vision inference on edge GPUs. We partner with breeding and animal-health companies and commercial producers to turn sensor streams into actionable, individual-level insights that improve genetics, welfare, and operational efficiency.
The Role We’re hiring a hardware systems engineer to lead the design and productization of an end‑to‑end edge platform that can reliably ingest
several hundred cameras
and
100 additional sensors
(UHF RFID readers, temperature sensors, weight scales, etc.), while running real‑time computer vision algorithms on high-performance edge compute and syncing clean, time‑aligned data to the cloud. You’ll own architecture, vendor selection, prototyping, testing, and manufacturing hand‑off.
What You’ll Do
Own the architecture
for a multi‑sensor edge system (rack/cluster switching storage power) that scales from single‑site pilots to multi‑site deployments.
Camera ingest @ scale : Design low-res camera networks (640x480p) for hundreds of streams; PoE/PoE budgeting; VLAN segmentation; IGMP snooping/multicast; clock sync (PTP/NTP); health monitoring and auto‑recovery.
RFID at scale : Specify and integrate ~100 UHF RFID readers/antennas; dense‑reader‑mode planning; anti‑collision tuning; shielding and interference mitigation; LLRP middleware; tag/antenna mapping and calibration.
Aux sensors : Integrate temperature, humidity, and environmental sensors and weigh scales with timestamping and calibration workflows.
Edge compute & CV pipelines : Size and tune GPU nodes; PCIe/NVLink/GPU‑direct considerations; containerized deployment; DeepStream/GStreamer pipelines; NVDEC/NVENC throughput planning.
Storage & data management : Architect local NVR‑style retention (e.g., 30–60 days) with RAID/NAS/DAS; plan write/read IOPS and sustained throughput; implement object storage gateways and offline‑first sync.
Power, thermal, reliability : Size PDUs/UPS/generators; 208/240 V distribution; rack layout, airflow, and thermal characterization; EMI/EMC and surge protection; observability (Prometheus/Grafana) and alerting.
Security & fleet ops : Network segmentation (802.1X, VLANs), certificates/PKI, secure boot, SBOM tracking; provisioning with Ansible/Terraform; OTA updates and device health.
Productization : DFM/DFT, BOM ownership and cost‑down, enclosure/industrial design, compliance (FCC/UL/CE as applicable), manufacturing partner coordination, pilot → scale playbooks.
Documentation & leadership : Write clear architecture docs, SOPs, and test plans; mentor junior engineers; collaborate with CV/ML, software, and field operations.
Minimum Qualifications
Experience building
production
multi‑sensor or video systems in industrial/retail/logistics, robotics, autonomous systems, or security/NVR at 100 stream scale.
Deep experience with
camera systems ,
GStreamer/DeepStream ,
CUDA , and GPU resource planning (NVDEC/NVENC, memory bandwidth, PCIe lanes, NUMA).
Hands‑on with
UHF RFID
readers (e.g., Impinj/ThingMagic), antenna planning, LLRP integrations, and dense‑reader deployments.
Strong
networking
chops: L2/L3 switching, PoE budgeting, 10/25/40/100 GbE links, QoS, multicast/IGMP, PTP/NTP time sync, and site‑to‑cloud backhaul.
Proficiency in
Linux
(driver bring‑up, udev, systemd),
Docker , scripting ( Python/Bash ), and at least one systems language ( C/C
or
Rust ).
Experience integrating
industrial sensors
and weigh scales; calibration/verification procedures.
Proven ability to take hardware from
architecture → prototype → pilot → manufacturing , with vendor selection and
BOM cost
ownership.
Nice to Have
Experience with
Triton Inference Server , Kafka/MQTT, ROS 2, or time‑series databases.
PCB/schematic literacy and lab skills (oscilloscope, spectrum analyzer); EMI/EMC troubleshooting.
Field‑hardening in harsh environments (dust, vibration, corrosive atmospheres) and IP‑rated enclosures.
Observability: Prometheus/Grafana, Loki, OpenTelemetry; remote fleet management.
Domain exposure to animal/AgTech, food production, or other regulated industrial settings.
What Success Looks Like (0–90 Days)
30 days : Detailed architecture & bill of materials for a 100 camera / ~100‑reader system; risk register and test plan.
60 days : Lab demo with ≥64 cameras & ≥20 RFID readers; validated ingest and CV throughput; PTP sync working across devices; storage and backhaul benchmarks.
90 days : Pilot‑ready reference rack with scripts/automation, monitoring dashboards, and install guides; factory acceptance test (FAT) checklist complete
Pragmatic, test‑driven engineering with measurable throughput/latency/reliability targets.
Field‑first: designs must be installable, serviceable, and cost‑effective.
We value clear writing, generous collaboration, and bias‑to‑prototype.
Compensation & Benefits Competitive salary equity; equipment budget; travel.
Interview Process
1) 30‑min intro screen
2) 60‑min technical deep dive (CV/ML pipelines networking RFID)
3) 90‑min system design exercise
4) Panel with team
5) References
Application Send a short note, résumé/LinkedIn, and a
1‑page case study
of your largest multi‑sensor deployment (camera/RFID counts, throughput, and lessons learned) to
jean@prophetai.io
with subject
“Hardware Systems Engineer”
#J-18808-Ljbffr