Senior GenAI Engineer: Build Agentic AI Equity

Hello, we’re Instrumentl.

We’re a mission-driven startup helping the nonprofit sector to drive impact, and we’re well on our way to becoming the most-loved grant discovery and management tool.

Instrumentl is a hypergrowth YC-backed startup with over 4,000 nonprofit clients, from local homeless shelters to larger organizations like the San Diego Zoo and the University of Alaska. We are building the future of fundraising automation, helping nonprofits to discover, track, and manage grants efficiently through our SaaS platform. We are cash flow positive and doubling year-over-year, with customers who love us (NPS is 65+ and Ellis PMF survey is 60+).

As a Sr. Software Engineer, GenAI at Instrumentl, you’ll own the full lifecycle of AI features—from rapid prototyping to production deployment and ongoing evaluation. You will build agentic LLM systems that can plan and use tools, implement RAG pipelines over our domain data, manage and evolve embeddings and indices, and stand up evaluation/observability so our AI is grounded, safe, and cost–effective.

Our ideal candidate is a strong software engineer, has experience building AI features and most importantly eager to learn, willing to experiment, and excited to empower both teammates and customers to accelerate social progress and propel innovation.

You’ll embed with one of the above groups in a hands‑on role, collaborating closely with Product and Design, while partnering with DTI on platform‑level AI capabilities.

The Instrumentl team is fully distributed (though if you’d like to work from our Oakland office, we would love to see you there). For this position, we are looking for candidates based in the United States who have significant overlap with Pacific Time Zone working hours.

• Design agentic systems & ship AI to production: Turn prototypes into resilient, observable services with clear SLAs, rollback/fallback strategies, and cost/latency budgets. Build tool‑using LLM “agents” (task planning, function/tool calling, multi‑step workflows, guardrails) for tasks like grant discovery, application drafting, and research assistance.

• Own RAG end‑to‑end: Ingest and normalize content, choose chunking/embedding strategies, implement hybrid retrieval, re‑ranking, citations, and grounding. Continuously improve recall/precision while managing index health.

• Collaborate cross‑functionally while raising engineering standards: Work side by side with Product, Design, and GTM on scoping, UX, and measurement; run experiments (A/B, canaries), interpret results, and iterate. Write clear, maintainable code, add tests and docs, and contribute to reliability practices (alerts, dashboards, incident response).

• Software engineering background: 7+ years of professional software engineering experience, including 1+ years working with modern LLMs (as an IC). Startup experience and comfort operating in fast, scrappy environments is a plus.

• Hands‑On: you’ve used Python (FastAPI or equivalent), Postgre
  
  SQL or any relational language, Redis etc, and you’re open to adopting new tools to get the job done.

• Data chops: Comfortable with SQL, schema design, and building/maintaining data pipelines that power retrieval and evaluation.

• Infrastructure & languages: Proficiency in Python (FastAPI, Celery) and Type Script/Node
  ; familiarity with Ruby on Rails (our core platform) or willingness to learn.
  
  Experience with
  AWS/GCP
  , Docker, CI/CD, and observability (logs/metrics/traces).

• LLM agentic systems: Experience building tool/function‑calling workflows, planning/execution loops, and safe tool integrations (e.g., with Lang Chain/Lang Graph, Llama Index, Semantic Kernel, or custom orchestration).

• Collaborative approach: You thrive in a cross‑functional environment and can translate research ideas into shippable, user‑friendly features.

• Results‑driven: Bias for action and ownership with an eye for speed, quality, and simplicity.

• RAG expertise: Strong grasp of document ingestion, chunking/windowing, embeddings, hybrid search (keyword + vector), re‑ranking, and grounded citations.

• Experience with
  re‑rankers/cross‑encoders
  , hybrid retrieval…