About Orderly Wellness
Orderly Meds is building a better, more supportive healthcare experience-one that's fast, reliable, and designed with people in mind. As a fully remote company, we've grown quickly by combining thoughtful technology with compassionate service, helping tens of thousands of patients get the care and medication they need, without the hassle.
We're a collaborative, mission-driven team working across the U.S. (and beyond!) to improve access to care at scale. With over 100 customer support agents, six pharmacy partners, and a 50-state clinical network, we're scaling fast, and looking for curious, kind, and solutions-oriented people to grow with us.
About the role
As an AI Engineer, you'll design, build, and deploy the generative AI systems that power OrderlyMeds. You'll fine-tune large language models, innovate with embeddings and RAG architectures, and optimize inference performance across diverse workloads.
You'll work closely with data scientists, AI platform engineers, and product teams to bring advanced models from prototype to production - building reliable, high-performance systems that make AI a core capability of the organization.
What you'll do
• Fine-tune and evaluate large language models using frameworks like Hugging Face Transformers, PyTorch, and TensorFlow.
• Build and deploy RAG (retrieval-augmented generation) systems, vector search pipelines, and embedding stores (e.g. FAISS, Pinecone, Weaviate).
• Develop robust inference infrastructure to support low-latency, high-throughput model serving.
• Integrate AI services via APIs and microservices using Python, FastAPI, or LangChain.
• Design evaluation pipelines to track model performance, drift, and prompt effectiveness.
• Collaborate with security, DevOps, and product teams to deploy production-grade AI systems in AWS, GCP, or Azure environments.
• Explore, test, and implement new generative model capabilities (e.g., multi-modal LLMs, instruction-tuning, fine-tuning pipelines).
Qualifications
• 2 - 5+ years of experience in machine learning, applied AI, or MLOps.
• Strong proficiency in Python, PyTorch, and TensorFlow.
• Experience fine-tuning or deploying LLMs, transformer-based architectures, or embedding models.
• Familiarity with containerized deployments (Docker, Kubernetes) and CI/CD workflows.
• Knowledge of vector databases, prompt optimization, and evaluation frameworks.
• A strong bias toward building, iterating, and shipping - not just researching.
The pay range for this role is:
80,000 - 120,000 USD per month (Remote (United States))
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