AI Engineer

Overview

At Parallel, we build AI agents to help healthcare facilities automate their administrative processes, starting with medical coding. We aim to reduce the 25% of healthcare spending lost to manual, repetitive work by providing AI agents that operate inside existing hospital tools with no integrations or disruption. Our first agent automates end-to-end coding workflows to free up time for medical staff to focus on patients. We launched in 2024 with backing from investors including Frst, Y Combinator, Hexa, Kima Ventures, and Better Angle.

The future of healthcare isn’t just digital — it’s automated. Come help us build it.

Mission

As a Founding AI Engineer, you will:

• Design and implement LLM-powered systems for automating medical coding
• Build and iterate on agent-based workflows tailored to complex clinical operations
• Collaborate on integrating AI outputs with user-facing apps used by doctors and hospital staff
• Work closely with hospital IT to build secure and scalable data ingestion pipelines adhering to health data security standards
• Partner with the CTO to define the AI roadmap and integrate state-of-the-art tools with robust backend infrastructure
• Own the full lifecycle of AI features: research, prototyping, evaluation, deployment, and monitoring

We’re looking for someone who is passionate about the ongoing AI revolution, action-oriented, autonomous, and ambitious. You are the ideal candidate if you have:

Qualifications

• 5+ years of experience working on applied ML/AI problems in production environments
• Strong experience with LLMs and NLP, including prompt engineering and/or fine-tuning
• Proficiency in Python or Node.js and experience with ML libraries (e.g. Hugging Face, LangChain, PyTorch, etc.)
• Familiarity with backend services (Node.js/TypeScript) and data infrastructure is a strong plus
• A deep sense of ownership and ability to move from prototype to product quickly
• Experience working with sensitive or regulated data (healthcare, finance, etc.) is a bonus

Technical stack

• Backend: TypeScript with NestJS, Express, Prisma, Postgres
• Frontend: React, TanStack, Tailwind
• Data: Python & Node (for low-level proxy servers)
• Tools: Monorepo, GitHub, GitHub Actions
• Infra: AWS, Azure, Cloudflare, Docker, Terraform, Kubernetes
• CI/CD: GitHub, GitHub Actions, Monorepo setup
• Observability: Datadog
• AI/ML: Hugging Face, LangChain

Engineering Mindset

• Data security is our foundation, given our work with sensitive health data
• We focus on solving user problems, not shipping features — deep product involvement is essential
• Full type safety from database to UI
• Rapid development with tools like Cursor
• Automated best practices with eslint, Prettier, Jest, and TypeScript
• We leverage the latest technologies and libraries but sometimes, old boring tech that does the job is what’s needed
• Infrastructure should empower — not block — product iteration