Software Engineer - Applied AI

About Niural Niural is the AI-native platform that unifies payroll, compliance, HR, and financial operations into a single platform, enabling companies to hire, pay, and manage teams across 150+ countries with speed, accuracy, and intelligence. Backed by Marathon, M13, and Inspired Capital, Niural is redefining the future of work and intelligent finance. Why Niural? If you are a person who was drawn into tech through the promise of creating cutting-edge tools for a more exciting future, but find yourself jaded with the fact that the world's smartest engineers, and the most capable models ever built, are spending their lives optimizing how frequently we click on ads, this is your opportunity to change that. We’re building a future that changes the way companies participate in the new era of the Internet and create truly global products as they participate in a digital economy. If you want to work with an aggressively ambitious team, put AI agents in charge of work that has never been automated before, and build the system of record companies used to hire, pay, and manage people across borders, moving billions of dollars in the process, help us build the future of work and intelligent finance.

We are hiring a Software Engineer, Applied AI to build the intelligence layer of the Niural platform. You will work on EMMA, our AI orchestration system, shipping agent workflows, retrieval pipelines, and document understanding systems that operate directly on payroll, tax, and compliance data. This is not a research role, and it is not a chatbot bolted onto a SaaS product. The systems you build take real actions on money movement and statutory filings, where “usually right” is a defect. You should have strong Python skills and hands-on production experience with LLM systems: retrieval-augmented generation, tool calling, structured output, and evaluation. We’re looking for someone who treats model behavior as an engineering problem, measures before shipping, and wants to own features end to end.

Design, build, and ship production LLM features including agent workflows, tool and function calling, MCP servers, and retrieval-augmented generation pipelines. Build retrieval systems over statutory guidance, tax authority publications, contracts, and employee handbooks, owning chunking strategy, embedding selection, hybrid search, reranking, and retrieval quality end to end. Build structured extraction pipelines for payroll and compliance documents such as paystubs, statutory filings, and PDFs, including schema enforcement, validation, and repair loops. Define and maintain evaluation infrastructure: golden datasets, regression suites in CI, LLM-as-judge calibration, and accuracy and hallucination rate tracked as first-class metrics. Optimize latency and cost per task through model routing, prompt and semantic caching, batching, and context window budgeting. Implement safety and correctness controls including PII detection and redaction, prompt injection defense, grounded citations, confidence thresholds, and escalation to human reviewers. Instrument and monitor AI systems in production using tracing and observability tooling, and debug model behavior with the same rigor applied to application code. Collaborate with payroll, tax, and compliance experts to translate regulatory requirements into system behavior, and with product and design to ship user-facing AI features. Stay abreast of a fast-moving field and bring back what is actually useful, with a bias toward measurable improvement over novelty.

3 plus years of professional software engineering experience, including recent production experience shipping LLM-backed features to real users. Strong Python skills and comfort in service-oriented codebases: APIs, queues, background workers, and observability. Hands-on experience with retrieval-augmented generation: embedding models, vector stores (pgvector, OpenSearch, Pinecone, or equivalent),