Founding Backend Engineer (AI Focus) - Atrix

Spur
New York CityPosted 21 February 2026

Job Description

Breakthrough medicines all start the same way: years of research, mountains of data, and countless decisions made by medical teams within the world's premier biopharma labs. These decisions then determine which treatments make it to market, how doctors learn about them, and ultimately which patients get access to life-saving care and when.We're at an inflection point. AI-powered drug discovery, precision medicine, and an explosion of real-world evidence have accelerated what's possible, but the teams translating science into patient access are drowning in data they can't use. The tools haven't kept up with the stakes.Atrix builds purpose-built AI for life sciences, designed for the accuracy and rigor biopharma requires. We're live with top-10 pharmaceutical companies, scaling fast, and focused on one thing: helping the right treatments reach the right patients, soonerThe Role DescriptionYou’ll be a core builder of Atrix’s backend infrastructure, powering AI-native workflows that ingest unstructured data, structure it with AI, and deliver insights that matter—instantly and reliably.You’ll work on scalable data pipelines, robust APIs, and real-time inference systems, supporting everything from complex agent workflows to customer-facing dashboards. Whether it’s building retrieval pipelines, optimizing async background workers, or designing clean, secure APIs, your work will be foundational to how our platform works and scales.We're building a modular, agentic platform that connects to customer-approved data sources, transforms unstructured data using LLM-based agents, and delivers decision-ready intelligence to regulated teams.You’ll tackle rich backend engineering challenges like:⚙️ System Design & Reliability – Scale to handle massive amounts of records with fast response times🧠 LLM Ops – Make AI outputs explainable, auditable, and efficient🛠 Multi-Tenant Infra – Isolate customer data across parallelized pipelines🚀 AI Performance Tuning – Optimize inference cost, caching, and latency📡 RAG Pipelines & Contextual Workflows – Connect structured + unstructured data for real-world usageYou’ll work closely with the Engineering Lead and CEO, and collaborate across product, AI, and GTM. As one of our earliest backend engineers, you’ll influence everything from our architecture to hiring bar.💡 This is an in-person role based in NYC, with flexibility for remote work on some days as needed.ResponsibilitiesThis role is perfect for someone who thrives on solving deep technical problems, loves optimizing systems for speed and scale, and gets energized by seeing their work used in real-world, high-stakes environments. If you’re excited about powering AI workflows behind the scenes—and building infrastructure that’s as elegant as it is reliable—we’d love to talk.In this role, you will:Design and own major modules of our backend architecture (APIs, workers, orchestration)Optimize performance and reliability of data processing and AI inference systemsBuild and maintain LLM-powered backend features and multi-step workflowsCollaborate with product and AI teams to launch performant, production-grade AI featuresImprove observability, traceability, and modularity of backend systemsLead customer-focused feature delivery with high attention to security and complianceMust haves5+ years backend engineering experience in fast-paced product teams2+ years of Python experience, including async programmingExpertise with one or more frameworks: FastAPI, Flask, DjangoStrong experience with SQL (PostgreSQL) and ORMs (SQLAlchemy or equivalent)Experience building APIs, background workers, and distributed systemsUnderstanding of how LLMs, embeddings, or RAG workflows functionStartup experience or demonstrated ownership in complex backend projectsNice to havesFamiliarity with Celery, LangChain, LlamaIndex, or other LLM orchestration toolsExposure to RAG or context injection workflowsExperience with AWS, Terraform, Kafka, or PyTorchBackground in healthcare, data ... (truncated, view full listing at source)
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