We are hiring across research, data engineering, machine learning, and network infrastructure. Those are not four departments that meet at a standup; they are four faces of one problem, which is why the team is deliberately small and the scope per person is deliberately large.
Distributed collection at the edge means running listening infrastructure across every region we cover and treating a quiet node as a correctness bug rather than an alert. Real-time machine learning means models that judge urgency, authenticity, and data quality inline, in the milliseconds a record spends in flight, at volumes where a slow model is the same as a wrong one. Point-in-time correctness means an archive that never lets a corrected value leak backwards into what a backtest thought was knowable. And delivery means our own edge network, serving every client from their nearest point of presence over post-quantum encrypted transport.
None of these has a vendor you can buy your way out of, which is the reason the work is interesting and the reason we are careful about who does it.
