Build and deploy production AI that supports both consumer and clinician-facing experiences.

Design, develop, and maintain production-grade AI systems and APIs that power company products
Integrate and fine-tune machine learning models including LLMs, embeddings, and other ML architectures for internal and customer use cases
Own the end-to-end lifecycle for AI features: research and prototyping through productionization, deployment, and ongoing monitoring
Partner with engineering, product, and science teams to scope and ship AI-driven features
Create data pipelines, retrieval systems, and scalable infrastructure for inference and model serving
Deliver reliable AI performance in production by focusing on observability, monitoring, and reliability
(Depending on experience) Lead AI initiatives, influence architecture and system design decisions, and mentor other engineers

Proven experience building and deploying AI/ML systems in production at scale (not limited to prototypes or demos)
Strong understanding of model integration workflows, including inference pipelines, prompt engineering, fine-tuning, and RAG setups
Backend engineering experience with Python or Node.js preferred
Experience designing AI architectures such as hybrid retrieval systems, multi-model orchestration, or embeddings-based approaches
Hands-on experience with AWS cloud infrastructure and data pipelines
Solid API development and system architecture skills for scalable applications
Comfort working independently and driving execution in a startup environment
Experience integrating LLMs into production products (including OpenAI, Anthropic, and Vertex AI)
Familiarity with vector databases: Pinecone, Weaviate, FAISS, Chroma
Experience with observability, monitoring, and evaluation for AI systems
Experience leading projects and/or mentoring engineers
Prior experience in healthtech or biotech, or experience working with sensitive health data

Remote-first: core hours overlap from 9am–6pm CST, with shifts up to two hours either way; FTEs based in North America
Written-first communication: default to clear Slack posts; huddle when a thread stalls; meet only to make a real decision
Fast execution with quality focus: prioritize shipping correctly rather than “speed” that creates rework
Expect ownership over outcomes: no playbook waiting; write it as you go
Day One mentality with fewer titles and layers; process used as a guardrail where it counts
Transparent operations: numbers, misses, and feedback shared openly and in real time
Venture-paced environment with intensity in spikes and protected flexibility

Learn Fast, Get Better: find root cause and improve continuously
Be Relentlessly Resourceful: move fast, dig for answers, know when to ask for help
Act Like an Owner, Be Hungry to Win: act like it is your company
Act with Honesty and Empathy: share what’s true with care
Delight People by Anticipating Their Needs: solve the immediate problem and improve ahead of time

Location: Texas (remote)

AI Engineer

Job Description