Senior/Staff Software Engineer, Manufacturing Systems

About the Role

Atomic Machines is ushering in a new era of micromanufacturing with its Matter Compiler™ technology platform. This platform enables new classes of micromachines to be designed and built by providing manufacturing processes and a materials library that are inaccessible to semiconductor manufacturing methods. It unlocks MEMS manufacturing for device classes that could never be produced by semiconductor methods, as well as entirely new categories. The platform is fully programmable—like 3D printing—but operates as a multi-process, multi-material system: bits and raw materials go in, and complete, functional micromachines come out.

The Atomic Machines robotics fleet has reached a level of maturity where it's ready to bring the Matter Compiler online. Now, manufacturing software must be elevated to leverage this fleet into a true fab. Key architectural decisions—such as cloud vs. on-prem and the scope of the control layer—are currently being made in parallel without a shared, written-down source of truth. This role is for a seasoned engineer who can operate productively within this ambiguity: someone who will document design decisions, drive the team toward consensus, and establish testing, review, and traceability discipline to prevent future misalignment.

Responsibilities

• 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—including API development to govern machine behavior across the 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.
• Establish and model software engineering practices—testing, code review, CI/CD, documentation—appropriate for a team that's outgrown its current ones.
• Contribute to system reliability through structured observability, fault handling, and graceful degradation.
• Collaborate closely with mechanical, electrical, and process engineers to translate physical constraints into resilient software behavior.
• Partner with engineering leadership to help converge competing architectural visions into one well-reasoned direction.

Requirements

• 5+ years building or debugging systems with real external dependencies: hardware, embedded devices, networked services, or similar.
• A track record of making and defending nontrivial architecture decisions—build vs. buy, deployment topology, service boundaries—not just implementing someone else's design.
• Strong Python skills for production systems, plus proficiency in at least one systems or strongly-typed language (C++, Rust, or Go).
• Solid grounding in distributed systems fundamentals: state coordination, consistency, failure modes, concurrency.
• Experience introducing or maintaining CI/CD, automated testing, or observability tooling in a codebase that didn't already have it.
• Comfort operating against an incomplete or contested spec—and a bias toward driving clarity rather than waiting for it.
• Bachelor's degree in Computer Science, Electrical Engineering, Robotics, or related field, or equivalent experience.

Nice to Have

• Experience with real-time or resource-constrained environments, or analogous domains (IoT, edge compute, industrial systems, robotics, warehouses, manufacturing lines, fabrication and automation facilities).
• Thought seriously about observability: what to instrument, when logs aren’t enough, how to make failure legible, and how to structure data for ML/AI pipelines.
• Been the person who introduced testing or CI discipline to a team that didn't have it—and can speak concretely about how you got buy-in.
• Experience evaluating cloud vs. on-prem/edge deployment tradeoffs for latency- or safety-sensitive systems.
• Debugged issues that required reasoning across multiple system layers: application logic, transport, firmware, hardware.
• Genuinely energized by translating physical constraints—latency, noise, mechanical tolerance, safety margins—into software behavior.

Pay

Salary Range: $180,000 USD - $230,000 USD. The compensation for this position also includes equity and benefits.