Machine Learning Engineer

About Us Delty is building the healthcare’s AI operating system. We create voice-based and computer-based assistants that streamline clinical workflows, reduce administrative burden, and help providers focus on patient care. Our system learns from real healthcare environments to deliver reliable, context-aware support that improves efficiency and elevates the provider experience. Delty was founded by former engineering leaders from Google, including co-founders with deep experience at YouTube and in large-scale infrastructure. You’ll get to work alongside people who built massive systems at scale — a chance to learn a lot and contribute meaningfully from day one. We believe in solving hard problems together as a team, iterating quickly, and building software with long-term thinking and ownership. What You’ll Do • 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. What We’re Looking For • 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. • Comfort working in a fast-paced startup environment with high ownership and ambiguity. • Ability to clearly explain modeling choices, assumptions, and limitations to non-machine-learning stakeholders. Bonus • 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. • Prior startup experience or founder mindset — we value ownership, pragmatism, and bias toward shipping. • Experience with model monitoring, data drift detection, or ML infrastructure tooling. Why join • Learn from seasoned Google engineers: As former Google engineers who built systems at YouTube and Google Pay, we’ve operated at massive scale. Working alongside us gives you a chance to build similar systems and learn best practices, scale thinking, and software design deeply. • High impact: At a small but ambitious team, your contributions will influence architecture, product direction, and core features. You will have real ownership and see the effects of your work quickly. • Grow fast: We’re iterating rapidly; you’ll be exposed to the full stack, AI/ML pipelines, system architecture, data modeling, and product-level decisions — a fast-track to becoming a senior engineer or technical lead. • Challenging and meaningful work: We’re tackling the hardest part of software engineering: bridging AI-generated prototypes and robust, scalable enterprise-grade systems. If you enjoy thinking deeply about systems and building reliable, maintainable foundations — this is for you.