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Salary: USD 160,000 - 230,000 / annual
Constellation is building an AI-human translation layer to address deep problems in human experience: empowering people toward their goals, augmenting cognition and emotional wellness, and fostering mutual understanding. The company is generating a richly multimodal dataset to train a new class of foundation models focused on understanding what it means to be human, rather than merely capturing human knowledge.
As a Founding Data Infrastructure Engineer, you will own the complete backend architecture for data collection and processing. Your responsibilities span the full data pipeline: designing fault-tolerant ingestion engines that handle sensor connectivity issues and hardware failures without data loss; architecting a unified device abstraction layer to seamlessly ingest data from heterogeneous peripherals (USB, BLE, TCP/IP); optimizing I/O operations and tracking bandwidth/latency; defining storage topology for TB-scale daily ingestion; selecting efficient file formats for complex unstructured data (video, audio, text, timeseries); designing database schemas optimized for rapid indexing and retrieval; managing on-premise server provisioning and configuration; and building synchronization logic to move terabytes of data from edge buffers to cloud repositories.
You will collaborate closely with the AI team to ensure the data infrastructure delivers high-performance, training-ready datasets for foundation models. This is a founding-team role where you'll have significant architectural ownership and impact on the company's technical direction.
Required qualifications include 3+ years shipping production-grade backend systems; expert-level proficiency in Python, Rust, C++, or Go; strong experience with high-performance messaging/queuing tools (ZeroMQ, Kafka, RabbitMQ, Redis); cloud infrastructure expertise (AWS, GCP, Azure); experience with low-latency streaming protocols (WebRTC, RTSP, HLS); proficiency with video processing tools (FFmpeg, GStreamer, OpenCV) and codec knowledge; high-performance data transfer techniques (zero-copy networking, shared memory, memory-mapped files); time-series database experience (TimescaleDB, InfluxDB, ClickHouse); and solid networking fundamentals. Nice-to-have skills include familiarity with ML frameworks (PyTorch/TensorFlow), dataset optimization for GPU utilization, and interest in neurotech or human-AI interaction.
