Delty is building the healthcare’s AI operating system to streamline clinical workflows, reduce administrative burden, and help providers focus on patient care
Requirements
- At least 3 years of experience building and deploying machine learning systems in production
- Strong foundation in machine learning for structured (tabular) data, including feature engineering, regression or classification models, and ranking or prioritization problems
- Experience with the full machine learning lifecycle: data preparation, train/test splitting, evaluation, deployment, retraining, and monitoring
- Solid backend engineering skills: writing production-quality code, building services or batch jobs, and working with databases and data pipelines
- Good system design instincts: you understand trade-offs between model complexity, reliability, latency, scalability, and maintainability
- Experience working with healthcare or operational decision-support systems
- Experience building or integrating LLM systems in production, such as retrieval-augmented generation, fine-tuning, or structured prompting workflows
Responsibilities
- Build and own production machine learning systems end-to-end: from data modeling and feature engineering to training, evaluation, deployment, and monitoring
- Design and implement data pipelines that turn raw, messy real-world healthcare data into reliable features for machine learning models
- Train and evaluate models for ranking, prioritization, and prediction problems (for example, identifying high-risk or high-priority cases)
- Deploy models into production as reliable services or batch jobs, with clear versioning, monitoring, and rollback strategies
- Work closely with backend engineers and product leaders to integrate machine learning into real workflows and decision-making systems
- Make architectural decisions around model choice, evaluation metrics, retraining cadence, and system guardrails — balancing accuracy, explainability, reliability, and operational constraints
- Collaborate directly with founders and engineers to translate product and operational needs into scalable, maintainable machine learning solutions
Other
- Ability to clearly explain modeling choices, assumptions, and limitations to non-machine-learning stakeholders
- Comfort working in a fast-paced startup environment with high ownership and ambiguity
- Prior startup experience or founder mindset — we value ownership, pragmatism, and bias toward shipping
- Learn from seasoned Google engineers: As former Google engineers who built systems at YouTube and Google Pay, we’ve operated at massive scale
- High impact: At a small but ambitious team, your contributions will influence architecture, product direction, and core features
