Hivemapper is a decentralized global map data network built by 10s of thousands of mapping devices. High-res sensors like RGB, Stereo Depth, GNSS, IMU, etc. feed sensor fusion and ML models at the edge. Data is automatically uploaded in near realtime over LTE or WiFi. Enterprise tech, mapping, auto, robotaxis, rideshare, and entertainment represent some of the customers consuming data today.
APIs allow anyone to consume precisely extracted Map Features, HD map data, high-res street-level imagery, construction, and driving events for AV simulation. Tech-savvy customers develop and deploy software directly to our dashcams to get realtime data for things like change detection or visual semantic data mining. AI Fleet management tools drive value to large fleets of vehicles.
Data Platform
Every day, we process data from millions of KM from 10s of thousands of high resolution sensors deployed around the world. A symphony of different Sensor Fusion, ML, AI, and 3D Sensing processes are performed at the edge, feeding an AI conflation system on our processing clusters called Map AI.
The Platform Engineering team is responsible for the core distributed systems, ML infrastructure, data model abstractions, and data engine that powers our data customers, consumer products, and user-facing AI technology.
We strongly believe in engineering design principles around abstraction and programmability that allow us to move quickly with minimal manual interventions. Our work is fast-paced, collaborative, and data-driven.
Responsibilities
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Drive architecture, design, implementation, and eval of our core systems and data platforms
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Partner across the company to ensure our systems are adaptable to emerging opportunities
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Help plan technical roadmaps and lead higher level technical strategy
Requirements
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Experience building production critical systems used by >= thousands of users
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Deep backend experience with a large scale data platform, DB, ETL, distributed system, etc.
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Strong analytical skills in a relevant field like ML, math, statistics, quantitative economics, etc.
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Track record of deploying and maintaining large scale distributed systems
Nice to Haves
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Experience with geospatial data, HD mapping, sensor fusion, ADAS or AV, etc.
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Experience managing, generating, and processing large amounts of geospatial or imagery data
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Experience with Spark, EMR, MapReduce, Presto, Trino, Mongo Sharding, or similar systems
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Experience with blockchain/web3Experience scaling products or systems at hyper-growth startups