Senior/Staff Software Engineer, Distributed Systems

The Atomic Machines robotics fleet has reached a level of maturity where it's ready to bring the Matter Compiler online.

Now is the time manufacturing software must be brought up to par to leverage this fleet into a true fab.

Concretely:

• Architectural decisions — cloud vs. on-prem, how far the control layer should extend — are currently being made in parallel by people with different mental models of the end state, without a shared, written-down source of truth.
• The cost is real: engineering time is being spent today on work that may be thrown away once the architecture actually converges.

We're hiring a seasoned engineer who can operate productively inside that ambiguity — someone who will write the design docs, drive the team toward a decision, and also model (and insist on) the testing, review, and traceability discipline that keeps this from happening again.

This is as much a mandate to bring engineering rigor to the org as it is to build a platform that lets process developers — and eventually in-the-loop physical AI — safely program the fab.

If that sounds like more org-building than you want in a "software engineer" role, this probably isn't the right fit — and that's a useful thing to know before either of us invests time in the process.

• Design and build the distributed software systems that coordinate state, timing, and behavior across manufacturing hardware; write, test, and debug sensors, actuators, and process controllers under real-time and reliability constraints.
• Design workflows that bridge manual and automated process steps, and coordinate handoffs between production and process development.
• Evolve the existing system in place — including but not limited to API development to govern machine behavior across our fleet— as the architecture converges, without waiting for a clean-slate rewrite to start delivering value.
• Instrument systems so machine and process data is legible — not just to humans via logs, but structured for downstream AI/ML consumption.
• Investigate and resolve issues that span software, firmware, and physical systems.