Senior/Staff Software Engineer, Machine Control Systems

Job Description

Job Description

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 not only for device classes that could never be produced by semiconductor methods, but also for entirely new categories. Furthermore, this digital platform is fully programmable in the way 3D printing is digital—but whereas 3D printing produces parts of a single material using a single process, the Matter Compiler™ technology platform is a multi-process, multi-material system: bits and raw materials go in, and complete, functional micromachines come out. The Atomic Machines team has also created an exciting first device—made possible only through the Matter Compiler™ technology platform—that we will be unveiling to the world soon.

Our offices are in Emeryville and Santa Clara, California.

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.

What You'll Do:

• 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.
• 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, rather than waiting for it to be handed down.

What You'll Need:

• 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) [confirm against actual stack].
• 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.

Bonus Points For:

• Experience with real-time or resource-constrained environments, or analogous domains (IoT, edge compute, industrial systems, robotics, warehouses, manufacturing lines, fabrication and automation facilities).
• You've thought seriously about observability: what to instrument, when logs aren't enough, how to make failure legible, and you've instrumented systems specifically to make their data usable by ML/AI pipelines.
• You've 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, not just what you built.
• Experience evaluating cloud vs. on-prem/edge deployment tradeoffs for latency- or safety-sensitive systems.
• You've debugged issues that required reasoning across multiple system layers: application logic, transport, firmware, hardware.
• You're genuinely energized by translating physical constraints — latency, noise, mechanical tolerance, safety margins — into software behavior.

The compensation for this position also includes equity and benefits.

Salary Range

$180,000—$230,000 USD