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. #J-18808-Ljbffr