Senior Backend Engineer

Grants fund nonprofit work, but they also create some of the toughest operational challenges. Every grant brings its own deadlines, spending restrictions, reporting requirements, and commitments, so managing that complexity across fundraising, finance, and HR requires more than spreadsheets.

Instrumentl is the system of record for grants. We bring discovery, applications, award compliance, and grant operations into one platform, giving nonprofits a shared foundation to grow their revenue and impact. More than 6,000 nonprofits rely on Instrumentl, from local community groups to state and county governments, universities, and maybe even your local animal shelter.

We're a cash flow positive, YC-backed company building our next chapter, and we're hiring people who will shape and grow the impact of the nonprofit sector.

We're hiring a Senior Backend Engineer to own AI features end to end, from rapid prototype to production and the evaluation that keeps them honest. You'll build the APIs, tool-using agents, and RAG pipelines that turn frontier LLMs into grant discovery, application drafting, and research tools our 5,500+ nonprofits rely on every day. It's a high-ownership seat on a small team, where what you ship reaches customers fast and you help shape how we build AI here.

• Build tool-using LLM agents (task planning, function and tool calling, multi-step workflows, guardrails) for grant discovery, application drafting, and research assistance.
• Turn prototypes into resilient, observable services with clear SLAs, rollback and fallback strategies, and cost and latency budgets.
• Stand up evaluation and observability so our AI stays grounded, safe, and cost-effective.

• Write high-quality, thoroughly tested code across the backend and the data pipelines that power retrieval and evaluation.
• Contribute to reliability practices: alerts, dashboards, and incident response.

• Partner with Product, Design, and GTM on scoping, UX, and measurement.
• Run experiments (A/B, canaries), interpret results, and iterate.
• Raise engineering standards through clear, maintainable code, tests, docs, and thoughtful review.

• 7+ years building and shipping production backend systems in Python (FastAPI, Celery, or equivalent), taking features from prototype to production with real reliability practices like tests, observability, and rollback.
• Hands-on experience building LLM features in production: tool and function calling, multi-step agent workflows, and the guardrails and evals that keep them grounded, safe, and cost-effective. This is the core of the role.
• Strong data fundamentals: SQL, schema design, and building pipelines that power retrieval and evaluation.
• Thrives in a fast, scrappy startup environment with high ownership and a bias for action, speed, quality, and simplicity.

• Type Script and Node, plus familiarity with Ruby on Rails (our core platform) or a willingness to learn it.
• Experience with AWS or GCP, Docker, CI/CD, and observability (logs, metrics, traces).
• RAG depth: document ingestion, chunking and windowing, embeddings, hybrid search (keyword plus vector), re-ranking, and grounded citations.
• Experience with re-rankers and cross-encoders, hybrid retrieval tuning, or search and recommendation systems.
• Evaluation mindset: designing eval suites (RAG/QA, extraction, summarization) using automated and human-in-the-loop methods, with familiarity with frameworks like Ragas, Deep Eval, or OpenAI Evals.
• Orchestration frameworks:
  Lang Chain or Lang Graph, Llama Index, Semantic Kernel, or custom orchestration.

For US-based candidates, the…