Manager, Autonomy Evaluation, Data & Test

Who we are

Neros is a defense technology company rebuilding America’s drone industrial base.

We design and manufacture high-performance unmanned systems that are tested in combat, iterated at startup speed, and built at massive scale.

Our team culture is fast, hands-on, and obsessed with closing the gap between design and deployment.

As drones transform the character of warfare, Neros is delivering the systems the West needs to compete on the modern battlefield and deter the adversaries of democracy.

We’re hiring engineers, operators, and builders who want to move fast, take on extreme ownership, and get capability into the hands of warfighters in months, not years.

What you will be doing

Neros builds autonomy for drones that operate in contested environments where GPS and communications are denied.

The Autonomy Evaluation, Data & Test team owns how autonomy performance is measured across all Neros platforms: the regression and simulation infrastructure every software change is tested against, the data platform that ingests and indexes flight logs, the hardware-in-the-loop bench, and field test operations.

As the leader of this team, you will define the metrics and acceptance criteria that determine whether autonomy software is cleared to fly, and build the team and systems that produce that evidence.

• Own the release criteria for autonomy software, including the definition of the regression gate, the conditions under which a build is blocked, and the process for reviewing exceptions
• Define autonomy performance metrics and their semantics for the organization, and publish coverage and reliability reporting to engineering and company leadership
• Build and operate a multi-tier regression system spanning unit and module tests, open-loop replay against logged flights, closed-loop simulation, and hardware-in-the-loop testing on production flight computers, including latency and deadline-miss measurement on target hardware
• Own the autonomy data platform end to end: on-vehicle capture, field egress, ingest and normalization, log catalog and query layer, deterministic replay at scale, and versioned dataset materialization for model training
• Direct field test and autonomy integration, including instrumented test articles, autonomy payload integration, range operations, log recovery, and flight-readiness execution