
201 - 500 employees
Founded 1986
🍽️ Food & Beverage
📦 Logistics
🏭 Manufacturing
Food & Beverage • Logistics • Manufacturing
STARLIMS is a leading provider of laboratory information management systems (LIMS) and informatics solutions, serving various sectors including life sciences, consumer goods, and public health. With over 1,100 customers across more than 85 countries, STARLIMS simplifies laboratory operations, enhances efficiency, and improves data integrity through its comprehensive platforms. The company has been delivering innovative solutions since its inception in 1986, helping laboratories optimize their R&D and quality control processes.
🔥 7 minutes ago
Where this job comes from
Posted on STARLIMS's careers page, not through a job board
First seen by Remote Rocketship 7 minutes ago

201 - 500 employees
Founded 1986
🍽️ Food & Beverage
📦 Logistics
🏭 Manufacturing
Food & Beverage • Logistics • Manufacturing
STARLIMS is a leading provider of laboratory information management systems (LIMS) and informatics solutions, serving various sectors including life sciences, consumer goods, and public health. With over 1,100 customers across more than 85 countries, STARLIMS simplifies laboratory operations, enhances efficiency, and improves data integrity through its comprehensive platforms. The company has been delivering innovative solutions since its inception in 1986, helping laboratories optimize their R&D and quality control processes.
• Design and build the runtime for agents, including planning and execution loops, tool calling, state management, durable execution, and failure recovery • Build safe agent access to platform data and external systems • Design coordination, delegation, and handoff across agents and workflows • Make agent behavior versionable, testable, measurable, and regression-safe across releases • Build reusable primitives so new agents can be configured rather than rebuilt • Convert domain workflows from expert conversations into working agents • Ground agent decisions and outputs in authoritative enterprise data • Implement human-in-the-loop approval gates, override capture, uncertainty handling, and evidence for decisions • Use user corrections and overrides to improve agents • Build evaluation harnesses for multi-step agent behavior • Define production metrics for agent quality, reliability, latency, cost, and human intervention • Implement guardrails, fallbacks, timeouts, cost ceilings, observability, and tracing • Design safeguards against prompt injection, unsafe tool use, excessive permissions, and data leakage • Manage prompt evolution, model drift, and non-determinism across releases • Integrate agents with platform APIs and third-party enterprise systems • Build retrieval and context pipelines for reliable, permission-aware agent context • Design controlled execution paths with complete, traceable audit trails • Build and operate backend services on AWS • Own significant parts of system architecture and contribute to technical decisions • Contribute to infrastructure-as-code and deployment pipelines
• 6+ years of software engineering experience, including production systems • Strong understanding of LLM behavior, limitations, and failure modes, especially how errors compound across a multi-step run • Experience with LLM APIs, tool and function calling, and designing planning and execution loops • Experience evaluating and debugging non-deterministic systems • Solid backend and cloud experience (AWS or equivalent) • Proficiency in TypeScript and/or Python • Comfortable debugging across distributed and non-deterministic systems • Comfortable making explicit tradeoffs between accuracy, latency, reliability, and cost • Comfortable working in ambiguous problem spaces where the right architecture isn't obvious yet • Comfortable owning production systems end-to-end • Comfortable choosing conventional software over AI when AI isn't the right solution • Nice to have: C#, Microsoft .NET Framework • Nice to have: Tool and interop protocols such as MCP • Nice to have: Production LLM systems, including tool-using or multi-step agentic workflows beyond simple prompting and chat interfaces • Nice to have: Evaluation pipelines and metrics built specifically for agentic systems • Nice to have: Experience in regulated or domain-heavy systems (validation, audit trails, controlled change) • Nice to have: Retrieval and grounding techniques for supplying agent context • Nice to have: Workflow and durable-execution platforms (Temporal, Step Functions, n8n, etc.) • Nice to have: Containerization and orchestration (ECS, EKS, Kubernetes) • Nice to have: Infrastructure as Code (Terraform or similar)
• Equal Opportunity Employer • Consideration for employment without regard to race, creed, religion, color, national or ethnic origin, citizenship, sex, sexual orientation, gender identity and expression, genetic information, veteran status, age or disability status
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