About Autonomize AI
Autonomize AI
is building the intelligence layer for healthcare. Our Genesis platform replaces brittle, manual knowledge workflows with AI agents that reason, retrieve, and act - reducing administrative burden so clinicians can focus on patients.
We're looking for engineers who don't just
integrate
AI into software - they think in agents, design for inference, and treat LLMs as first-class infrastructure.
The Opportunity
You'll architect and ship across the full Genesis stack: agentic pipelines, backend APIs, data infrastructure, and clinical-facing UI. You'll work directly with founders and customers. You'll own things end-to-end.
This is not a role where you bolt AI onto existing CRUD. You'll be making foundational decisions about how intelligent systems are designed, evaluated, and operated at scale in a regulated industry.
You're a Fit If You Have
1) AI-native engineering is your default mode
You've built production systems where LLMs are doing real work - not demos, not PoCs
You've designed and shipped RAG pipelines, multi-agent workflows, or tool-using agents in production
You understand prompt engineering as an engineering discipline: versioning, evaluation, regression testing
You've instrumented AI systems for observability - latency, token usage, hallucination rate, drift
You can reason about model tradeoffs (context length, cost, latency, accuracy) and make architectural calls accordingly
You've worked with LLM SDKs (OpenAI, Anthropic, Bedrock, etc.) and agentic orchestration frameworks (LangChain, LlamaIndex, CrewAI, or similar)
2) You build robust backend systems
6 + years building production web applications from scratch
Deep Python proficiency; comfortable with FastAPI, Django, or Flask in production
Experience designing APIs that serve both humans and AI agents (tool schemas, structured outputs, streaming)
Async-first thinking: asyncio, task queues, event-driven architectures
Kafka, Redis, or ActiveMQ for real-time data movement
Postgres, Elasticsearch, MongoDB, or graph databases (Neo4j, TigerGraph) in production
3) You operate at cloud scale
Docker and Kubernetes in production - this is a hard requirement
At least one public cloud (AWS, Azure, GCP) with real operational experience
Microservices and cloud-native design patterns
You've been on-call. You know what a bad deploy feels like at 2am.
You can ship a frontend when the product demands it
React, TypeScript, or modern JS frameworks
Enough frontend fluency to build clinical interfaces without a dedicated frontend handoff
Bonus Points
Led a small engineering team - mentored, reviewed, unblocked
CKAD or equivalent Kubernetes certification
ML/DL model deployment experience (PyTorch, scikit-learn)
Built evaluation harnesses or used MLflow, LangSmith, or similar for AI observability
Healthcare domain experience (FHIR, HL7, clinical workflows)
What We Value Above Credentials
Bias toward action
- you ship, then iterate. You learn by doing, not by planning.
Owner mentality
- you don't wait for permission. You identify what needs to exist and build it.
Intellectual honesty
- you'd rather say "I don't know, let me find out". When unsure, seek the right information from your peers or leaders.
Async-first communication
- you write clearly, document decisions, and work well across time zones.
What You'll Get
Ground floor equity
in a VC-backed healthcare AI company growing fast
Full-stack ownership
- no handoffs, no silos, no "that's not my team"
Direct access to founders and customers
- your technical decisions will be seen and felt
Professional development budget
- conferences, courses, certifications, books
Flexible, remote-friendly culture
built around output, not hours
If you've been waiting for a role where AI isn't a feature - it's the foundation - this is it.
