We're hiring
Build the future of
optimization.
We're a small team obsessed with making quantum-inspired optimization accessible to every engineering team. If you want to work on hard problems that matter, read on.
Why NEROX
Deep technical problems
We work at the intersection of GPU computing, quantum algorithms, and operations research. You'll learn things you won't encounter anywhere else.
Small team, real ownership
Fewer than 10 people. Every engineer owns a critical part of the stack — not just a JIRA ticket. Your decisions ship to production.
Remote-first, async-first
No open-plan offices, no mandatory standups. We communicate in writing and respect deep work time.
Competitive compensation
Salary at the 90th percentile for your role and location, plus meaningful equity at an early-stage company with real traction.
Open roles
Senior CUDA Engineer
Own the GPU annealing kernel stack. You'll design parallel cooling schedules, memory layouts, and warp-level primitives that extract maximum throughput from A10G and A100 hardware.
Quantum Algorithms Researcher
Research and implement next-generation QAOA variants, tensor network methods, and hybrid quantum-classical optimization algorithms. Requires a PhD in physics, CS, or mathematics with quantum computing focus.
Backend Engineer — Solver Infrastructure
Build the systems that dispatch, monitor, and meter GPU solver jobs at scale — job scheduling, WebSocket streaming, billing metering, and the Modal integration layer.
Developer Advocate
Write content that helps engineers understand and use quantum-inspired optimization. Own the SDK, quickstart guides, example notebooks, and developer community.
Solutions Engineer
Help enterprise customers solve their most complex combinatorial problems. You'll understand customer problem formulations, prototype QUBO models, and demonstrate solver performance.
Don't see a fit? We're always interested in exceptional people.
Send an intro to support@driftrail.com →GPU-native quantum-inspired optimization for combinatorial problems at any scale.