FogHorn

Industrial IoT Edge Intelligence Platform

Silicon valley

51 - 100 employees

26 - 50 engineers

$25m - $50m funding

Series c

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FogHorn’s Lightning™ product portfolio brings a groundbreaking dimension to IIoT and edge computing by embedding edge intelligence as close to the source of streaming sensor data as possible. The FogHorn platform is a highly compact, advanced and feature-rich edge intelligence solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage.

Active Roles

Principal Platform Engineer Silicon Valley, CA, United States

Sr Staff Software Engineer ( C++) Silicon Valley, CA, United States

Why join us?

100% year over year revenue growth since founding the company.
Tremendous momentum, growth, and referrals from our customers and partners.
Funded by well-known investors and industrial partners (Intel, GE, Honeywell, Bosch etc).
Official edge intelligence partner for the top cloud computing companies like Google and multiple large Industrial IoT incumbents like Honeywell.
Working on the most cutting edge real-time streaming analytics and machine learning for doing full-blown cloud like analytics at the edge (gateways, PLC etc) and mobile devices built from grounds up for IoT
A high-performance, expressive and low-footprint flow-reactive language and compiler for sensor stream analytics and running ML models; empowering advanced analytics as easily as describing it verbally in English.
Optimal code generation from ML model/meta-data (e.g. PMML/PFA/Tensorflow) descriptions to runtime.
Bi-directional flow of insights and ML models from edge <—> cloud for continuous learning.
End-to-end management, orchestration, auto-detection, and hassle-free deployment.

Engineering at FogHorn

Engineering team and processes

Our engineering team operates on two-week sprints and plan delivery for each quarter. We ship product every quarter to customers. Each sprint begins with a meeting to discuss time estimates for features and ends with a demo meeting where each engineer can show off the work they've completed. Engineers typically work with a designer and any other interested parties as a feature team to spec a feature, then work on the feature individually. We use Pull Requests on GitHub to conduct code reviews. All engineers are also empowered to push code to production, which we often do multiple times a day. We love test-driven incremental development.

Technical Challenges

We're building real-time analytics and machine learning platform that needs to handle thousands of events per millisecond at the edge in low-memory footprints and extremely low-latency requirements.
Solving for out of box integration with multiple IoT sensor protocols.
Writing efficient runtime and compilers to transform ML models build into the cloud to run at the edge and also perform closed-loop machine learning with the cloud.

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