Software Engineer, AI/ML, GenAI

Emploi pas sur LinkedIn

🕒 il y a 4 mois

Postuler Maintenant

Trouver des Emplois à Distance Similaires

Recevoir des emails pour des emplois à distance

📊 Vérifiez votre score de CV pour ce poste

Améliorez vos chances d'obtenir un entretien en vérifiant votre score de CV avant de postuler.

Télécharger le CV

Instrumentl

Site WebLinkedInTous les Emplois

11 - 50 employés

Fondée en 2016

🤝 À but non lucratif

⚡ Productivité

☁️ SaaS

💰 Seed Round en 2017-07

Non-profit • Productivity • SaaS

Instrumentl est une plateforme complète de gestion de subventions qui simplifie le processus pour les organisations à but non lucratif, les consultants en subventions et les universités afin de découvrir, postuler et gérer des subventions. Elle offre une vaste base de données de plus de 400 000 bailleurs de fonds et plus de 20 000 subventions sélectionnées pour aider les utilisateurs à trouver rapidement les opportunités de financement les mieux adaptées. Instrumentl améliore également la rédaction de subventions avec la technologie de l'IA, automatise les délais, gère les tâches et propose des outils pour la collaboration et la gestion de documents. Fiable pour plus de 4 000 organisations à but non lucratif, Instrumentl a facilité plus d'un milliard de dollars de subventions actives, soutenant une gamme de missions critiques.

Description

• 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).

🎯 Exigences

• 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.

🏖️ Avantages

• 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.