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.
Responsibilities
- Architecting, building and developing large-scale infrastructure, distribute systems and networks; training other teams on these systems
- Researching and developing new technologies in large scale decentralized computer and web3 systems; integrating with core systems
- Applying expertise with data structures or algorithms in an academic setting to create core abstractions for 100s of thousands of users and 100s of millions of square miles of data
- Working closely with operations, product development, and other engineering teams to deliver data-intensive cross-functional platform solutions
- Building auditable and observable systems that can robustly handle billions of video frames each day
- Helping to foster engineering excellence across backend development
Qualifications
- Master’s degree in Computer Science, Software Engineering, or a closely related field or foreign equivalent
- 2 years (24 months) of experience with each of the following:
- Building scalable, fault-tolerant, and high-performance distributed systems; infrastructure automation and optimization.
- Advanced data structures, indexing, partitioning, replication, horizontal scaling, and fault tolerance.
- Translating business needs into technical solutions, mentoring engineering teams, and fostering technical excellence.
- Implementing logging, monitoring, tracing, audit trails, and data compliance processes.
- Tools and technologies: Docker, Terraform, Apache Spark, Hadoop, PostgreSQL/PostGIS, Redis, Prometheus, Grafana, Git, AWS Lambda, AWS ECS, AWS Fargate, AWS EC2, AWS EMR, AWS Glue, AWS S3, and Node.js.
- Programming languages: Rust, Python, SQL/NoSQL, JavaScript, and TypeScript.
-Experience may be gained concurrently and may have been gained pre-, during, or post-master's
degree.
