Staff Software Engineer, Agentic Factory and Operations Software

Role Summary

This role is an opportunity to work with a high-agency startup team operating in the hard-tech and energy systems space. You will play a critical role in designing, building, and shipping the full stack of Amperesand’s ERP platform, company intranet, and the internal tools that our manufacturing, supply chain, finance, and corporate teams rely on every day.

This is a hands-on role that spans the entire stack. You will design data models and backend services, then build the React and TypeScript interfaces that sit on top of them — owning features end to end from requirements through production. The work covers ERP domains such as inventory, procurement, work orders, and reporting, alongside intranet capabilities such as employee directories, document and knowledge management, internal workflows, approvals, and self-service tools. You will also help introduce AI-assisted capabilities into these products, such as natural-language search over internal knowledge, document summarization, and workflow assistance.

The role requires close collaboration with product, design, backend, and domain expert teams — along with a strong sense of ownership, product judgment, and pragmatism in building reliable software for real-world operations. Beyond building new capabilities, you will also own and support our existing software products in production — keeping them healthy, responsive, and continuously improving for the internal stakeholders and customers who depend on them daily. This is a fast-paced environment with a small team and a large surface area, so we are looking for a go-getter who takes initiative, moves with urgency, and is willing to put in the hard work to see things through. AI is a first-class part of this roadmap rather than a side project: you will design and ship AI-powered capabilities integrated directly into the products themselves, and help set the standard for how we build them responsibly on top of sensitive internal company data.

This role is open at either the Senior or Staff level. Senior engineers are expected to own significant features and systems end to end and lift the engineers around them. Staff engineers are expected to do that while also setting technical direction across the ERP, MES, and intranet platforms, leading initiatives that span multiple teams, and taking responsibility for the long-term health of the architecture. We will calibrate the level to your experience during the interview process.

Responsibilities

• Own the design, architecture, and delivery of full stack features across Amperesand’s ERP platform, intranet, and internal operational tools.
• Build and scale backend services in Go that support mission-critical manufacturing, supply chain, and enterprise business workflows.
• Build responsive, accessible, and performant web interfaces in React and TypeScript that make complex operational data understandable and actionable.
• Design robust APIs, service interfaces, and data models that serve web applications, analytics systems, internal tools, and third-party enterprise integrations.
• Deliver ERP capabilities spanning inventory management, procurement, supply chain operations, work order execution, production tracking, and enterprise reporting.
• Build intranet features such as employee directories, document and knowledge management, internal announcements, approval and request workflows, onboarding flows, and self-service portals.
• Integrate the intranet and ERP with internal systems such as identity and SSO providers, HR systems, document storage, and collaboration tools.
• Design, build, and ship AI-powered features directly into our ERP and intranet products — natural-language search and Q&A over company knowledge, document understanding and data extraction, summarization, exception and anomaly flagging, and assistant-style workflows that remove manual operational work.
• Build the backend foundations behind those features: retrieval and embedding pipelines, vector and hybrid search, context assembly from ERP and intranet data, prompt orchestration, and tool/function calling into existing services.
• Integrate LLM providers and AI services into production systems with attention to latency, cost, caching, rate limits, fallback behavior, and graceful degradation when a model is slow, unavailable, or wrong.
• Design AI experiences that fit enterprise software — streaming responses, citations back to source records, clear handling of uncertainty, and human-in-the-loop review before any action is committed to the system of record.
• Apply guardrails for AI operating on internal company data, including permission-aware retrieval, access control, PII handling, auditability, and prevention of data leakage across teams.
• Partner with the AI team to evaluate models, build eval sets and quality metrics, and take promising prototypes all the way into supported, production-grade product features.
• Continuously evaluate where AI can meaningfully reduce manual effort across ERP and intranet workflows — and where a conventional solution is the better answer.
• Support, maintain, and continuously improve our existing ERP, MES, and intranet software products in production — triaging issues, diagnosing root causes, and shipping fixes and enhancements alongside new feature development, including time-sensitive issues that block production on the shop floor.
• Serve as a technical point of contact for internal stakeholders and customers using our platforms, responding to requests, unblocking users, and turning their feedback into prioritized product improvements.
• Build systems that are reliable, observable, secure, and resilient under production load, with appropriate attention to authentication, authorization, and data privacy for internal enterprise data.
• Optimize performance across both frontend rendering and backend latency, throughput, and data access patterns.
• Collaborate closely with product, design, and domain teams to translate operational requirements into well-scoped, well-architected software.
• Drive best practices for code quality, testing, deployment, reliability, and maintainability across the stack.
• Contribute to — and at the Staff level, help set — technical strategy and architecture decisions across frontend, backend, data, and platform layers.
• Mentor other engineers through code reviews, design reviews, pairing, and strong engineering standards.
• Lead multi-quarter technical initiatives that cross team and system boundaries, breaking them into deliverable milestones and keeping them on track.
• Establish and evolve engineering standards, patterns, and shared foundations that raise the quality and velocity of the whole team.
• Act as a force multiplier: grow other engineers, improve how the team works, and step into the hardest or most ambiguous problems on the roadmap.
• Partner with leadership and cross-functional stakeholders to shape the ERP, MES, and intranet roadmap, and to make pragmatic build, buy, and sequencing calls.
• Leverage AI-assisted development tools where useful to improve engineering velocity, code quality, and developer productivity.
• Document designs, architecture decisions, and tradeoffs for cross-functional stakeholders.

Qualifications

• 5+ years of professional software engineering experience building and shipping full stack web applications to production
• Demonstrated technical leadership without formal authority — driving alignment on designs, raising the engineering bar, and unblocking others. At the Staff level, this includes owning the architecture of a major platform area end to end.
• Strong experience building production backend services, ideally in Go; strong engineers from other typed backend languages who are eager to work in Go are welcome.
• Strong frontend experience with React and TypeScript, including modern component patterns, state management, and building complex data-dense interfaces such as tables, forms, dashboards, and workflow builders.
• Solid understanding of API design, relational data modeling, authentication and authorization, and service integration patterns.
• Experience building enterprise or internal software such as ERP, MES, intranet, workflow, admin, or business operations platforms.
• Hands-on experience with Manufacturing Execution Systems (MES) — building, extending, integrating with, or supporting them in a production manufacturing environment.
• Working knowledge of core MES domains such as work order and routing execution, electronic travelers and work instructions, WIP and production tracking, operator and station interfaces, labor and material consumption, and shop-floor data collection.
• Understanding of quality and traceability requirements in manufacturing software — in-process inspections, non-conformance and CAPA handling, serialization, unit genealogy, and as-built records.
• Familiarity with MES-to-ERP integration patterns, including synchronization of work orders, BOMs and routings, inventory and material movements, and production status between systems.
• Exposure to equipment and shop-floor integration — machine, PLC, SCADA, or IoT data collection — and to production metrics such as OEE, yield, cycle time, and downtime tracking.
• Experience with databases and data systems such as PostgreSQL, ClickHouse, Redis, or similar technologies, including schema design and query optimization.
• Familiarity with cloud infrastructure and containerized deployment environments such as AWS, Kubernetes, and Docker.
• Experience with observability and production operations, including logging, metrics, tracing, alerting, and incident debugging.
• Strong testing discipline across unit, integration, and end-to-end validation.
• Comfortable working with AI coding tools such as Copilot, Cursor, Claude, or similar tools to supplement and accelerate development.
• Hands-on experience integrating LLM or AI APIs into production application features — such as retrieval-augmented search, summarization, structured data extraction, or tool-calling workflows — along with sound judgment about where AI is and is not the right solution.
• Working understanding of retrieval and grounding techniques (embeddings, vector or hybrid search, chunking, context assembly) and of prompt design, evaluation, and iteration.
• Ability to work directly with non-technical stakeholders to gather requirements and turn ambiguous operational problems into shipped software.
• Thrives in a fast-paced startup environment — a self-starter and go-getter who takes initiative, moves quickly without waiting to be told what to do, and is willing to put in the hard work required to ship on aggressive timelines.
• Comfortable balancing new product development with ongoing support of live systems, and able to reprioritize as business needs shift.
• Proficiency in English and ability to communicate effectively with wider teams.
• Ability to travel up to 20%, including international.

Bonus Qualifications

• Hands-on experience shipping LLM or AI-powered features in production applications — for example retrieval-augmented generation, semantic or hybrid search, document understanding, structured data extraction, or assistant- and agent-style workflows.
• Familiarity with embeddings and vector search tools such as pgvector, Qdrant, Pinecone, or similar, including chunking strategies, indexing, and retrieval quality tuning.
• Experience with function and tool calling, agent frameworks, or protocols such as MCP that let AI systems act safely against existing APIs and services.
• Experience building evaluation and observability for AI features — eval sets, quality metrics, human review loops, prompt versioning, and regression tracking.
• Experience with model selection and cost/latency tradeoffs, or with fine-tuning and adapting models for domain-specific tasks.
• Experience in manufacturing, industrial automation, supply chain, inventory management, energy systems, or other operational technology domains.
• Background in energy engineering systems (power systems, energy storage, power electronics, or controls).
• Experience with enterprise identity, SSO, SAML/OIDC, or role-based access control at scale.
• Experience working within or building design systems and shared component libraries.
• Experience with real-time data, telemetry pipelines, or dashboards and reporting systems.
• Experience integrating with machines, PLCs, IoT devices, or external enterprise platforms.
• Familiarity with CI/CD workflows and software DevOps practices.
• Experience working with hardware, firmware, or operations teams.
• Open-source contributions a plus.