AI Engineering Lead at Aios Medical — Remote, $125k-$225k/yr inc equity

Posted about 1 month agoViewed

80000 - 120000 USD per year

US, UKFull-TimeAI Engineering

Company:Aios Medical (YC W20/S21)

Location:US, UK, US timezones, European timezones

Languages:English

Seniority level:Lead, 6+ years

Experience:6+ years

Skills:

AWSBackend DevelopmentDockerLeadershipPythonSoftware DevelopmentArtificial IntelligenceCloud ComputingFrontend DevelopmentMachine LearningReact.jsTypeScriptAPI testingData engineeringData scienceData StructuresCI/CDRESTful APIsA/B testing

Requirements:

6+ years of highly relevant experience in software engineering with a focus on AI and machine learning
Strong experience building production applications powered by LLMs (prompt and context engineering, RAG systems, model fine-tuning and evaluation frameworks)
Strong programming skills using Typescript and/or Python
Strong experience designing and building modern, customer-facing APIs
Comfortable building user-friendly interfaces using React or similar frameworks
Experience leading engineering team members

Responsibilities:

Build and lead AI systems across the company
Ship production AI while keeping it simple, reliable, and useful
Work across clinical, growth, and operations wherever AI can improve performance
Build a model-agnostic AI platform for seamless model switching
Set up A/B testing for performance and cost optimization
Create intelligent routing for matching tasks to models
Implement comprehensive monitoring and ensure high reliability with fallbacks
Drive AI adoption across the clinical operating system
Automate manual processes to improve efficiency
Create AI assistants to enhance the clinical team
Measure impact rigorously to improve patient outcomes and operational efficiency
Leverage AI for growth initiatives like personalized outreach and AI sales agents
Source and adapt new AI tools (video generation, voice synthesis)
Target human-level conversion rates with minimal manual touchpoints
Be data-obsessed: study datasets, build MVPs, run controlled tests
Create feedback loops to evaluate performance
Scale winners, kill losers, and let data drive decisions