Software Engineer, AI/ML, GenAI

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Instrumentl

WebsiteLinkedInAlle Stellen

11 - 50 Mitarbeiter

Gegründet 2016

🤝 Non-Profit

⚡ Produktivität

☁️ SaaS

💰 Seed Round im 2017-07

Non-profit • Productivity • SaaS

Instrumentl ist eine umfassende Plattform für das Fördermittelmanagement, die den Prozess für gemeinnützige Organisationen, Fördermittelberater und Universitäten vereinfacht, Fördermittel zu finden, zu beantragen und zu verwalten. Sie bietet eine umfangreiche Datenbank mit über 400. 000 Geldgebern und mehr als 20. 000 kuratierten Fördermitteln, um Nutzern zu helfen, schnell die am besten geeigneten Finanzierungsmöglichkeiten zu finden. Instrumentl verbessert auch die Erstellung von Förderanträgen mit KI-Technologie, automatisiert Fristen, verwaltet Aufgaben und bietet Werkzeuge für die Zusammenarbeit und Dokumentenmanagement. Vertraut von über 4. 000 gemeinnützigen Organisationen, hat Instrumentl mehr als 1 Milliarde US-Dollar in aktiven Fördermitteln ermöglicht, um eine Vielzahl von kritischen Missionen zu unterstützen.

Beschreibung

• 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. • Manage embeddings at scale: Select, evaluate, and migrate embedding models; maintain vector stores (e.g., pgvector/FAISS/Pinecone/Weaviate/Milvus/Qdrant); monitor drift and rebuild strategies. • Fine‑tune & build evaluation: Run SFT/LoRA or instruction‑tuning on curated datasets; evaluate the ROI vs. prompt engineering/model selection; manage data versioning and reproducibility. Create offline and online eval harnesses (helpfulness, groundedness, hallucination, toxicity, latency, cost), synthetic test sets, red‑teaming, and human‑in‑the‑loop review. • 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).

🎯 Anforderungen

• Software engineering background: 5+ years of professional software engineering experience, including 2+ years working with modern LLMs (as an IC). Startup experience and comfort operating in fast, scrappy environments is a plus. • Proven production impact: You’ve taken LLM/RAG systems from prototype to production, owned reliability/observability, and iterated post‑launch based on evals and user feedback. • LLM agentic systems: Experience building tool/function‑calling workflows, planning/execution loops, and safe tool integrations (e.g., with LangChain/LangGraph, LlamaIndex, Semantic Kernel, or custom orchestration). • 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 tuning, or search/recommendation systems. • Embeddings & vector stores: Hands‑on with embedding model selection/versioning and vector DBs (e.g., pgvector, FAISS, Pinecone, Weaviate, Milvus, Qdrant).IDocument processing at scale (PDF parsing/OCR), structured extraction with JSON schemas, and schema‑guided generation. • Evaluation mindset: Comfort designing eval suites (RAG/QA, extraction, summarization), using automated and human‑in‑the‑loop methods; familiarity with frameworks like Ragas/DeepEval/OpenAI Evals or equivalent. • Infrastructure & languages: Proficiency in Python (FastAPI, Celery) and TypeScript/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). • Data chops: Comfortable with SQL, schema design, and building/maintaining data pipelines that power retrieval and evaluation. • Collaborative approach: You thrive in a cross‑functional environment and can translate researchy ideas into shippable, user‑friendly features. • Results‑driven: Bias for action and ownership with an eye for speed, quality, and simplicity.

🏖️ Vorteile

• 100% covered health, dental, and vision insurance for employees, 50% for dependents • Generous PTO policy, including parental leave • 401(k) • Company laptop + stipend to set up your home workstation • Company retreats for in-person time with your colleagues • Work with awesome nonprofits around the US. We partner with incredible organizations doing meaningful work, and you get to help power their success.