Build the connection between hardware data and engineering decisions.
Loop Machines is building Loop: continuous engineering intelligence for mission-critical hardware. We connect requirements, configurations, tests and operational data in a shared engineering model, so engineers can understand changes, investigate failures and decide what to verify next.
Our goal is to help teams deploy fleets of mission-critical vehicles faster and operate them more reliably with agentic AI. We're building agents that connect engineering evidence with operational data to support verification, investigate failures and propose improvements for engineers to review.
We're looking for a founding full-stack engineer to turn that connection into software engineers can use. You'll work directly with the founder, with substantial ownership of technical decisions and delivery within an agreed product scope. This is an early-stage role, building from existing prototypes and software foundations.
What you'll work on
- Own complete workflows across the interface, backend APIs, database and deployment, and improve them through direct user feedback.
- Connect engineering records and telemetry to the correct assets, configurations, requirements and source evidence.
- Build reliable ingestion and background processing that can recover from interruptions, duplicate inputs and incomplete data.
- Implement reproducible checks and make results, evidence and coverage gaps clear to users.
- Integrate AI agents that investigate evidence and propose actions through typed tools, permissions, evaluation and human review.
- Establish practical engineering habits: automated checks, safe migrations, useful observability, access controls and tested recovery.
Your first focus will be one complete Verification & Change workflow: connect a selected source, resolve the relevant engineering context, run an approved check and help an engineer review the result. Over time, the same foundation will support fleet deployment and operational workflows.
What you'll bring
- End-to-end product ownership. You've shipped and maintained software used by other people, can deploy and debug what you build, and can explain what you personally owned and learned.
- Production Rust and strong data-modeling skills. You've built and maintained backend services in Rust, including asynchronous processing and error handling. You're comfortable with APIs, SQL, transactions, schema evolution and relational databases such as PostgreSQL.
- Practical full-stack ability. You can independently build a useful TypeScript interface with React or a comparable modern web framework, including asynchronous progress, errors and review states.
- Reliability and testing judgment. You can reason about retries, idempotency, partial failures and reproducible results, and turn explicit rules into tested code.
- Hands-on AI integration. You can demonstrate a bounded tool-using workflow with permissions, human review and evaluation of failure cases. An evaluated prototype is relevant evidence; model training experience is optional. Agents do not make uncontrolled physical decisions.
- Judgment and communication. You can scope useful work, explain trade-offs clearly and work with domain experts when requirements are incomplete.
- Relevant domain experience — a plus. Telemetry or time-series data, event processing with NATS/JetStream or Kafka, robotics or industrial software, and versioned engineering models.
- Additional technical experience — a plus. Python for analysis and evaluators, or controlled cloud, on-premises and air-gapped deployments. We do not expect experience in every additional area, hardware protocol or lifecycle stage.
Why this role
You'll help shape the foundations of a young product, work directly with the founder and connect software decisions to tangible engineering problems. The role offers broad technical ownership and the opportunity to influence how we build as the team develops.