Top AI & Machine Learning Jobs

Open application for roles across engineering, product, operations, and customer success at an AI-native EMR startup. Candidates may build AI-agent workflows, improve clinical operations, support implementations, or drive growth and adoption. Emphasis on communication, ownership, and curiosity about AI and healthcare.

Leads end-to-end data science initiatives, developing and deploying scalable machine learning and AI solutions for crop protection product development. Responsibilities include opportunity identification, data preparation, model development, evaluation, production deployment, emerging AI technology evaluation, stakeholder collaboration, technical leadership, mentoring, and communicating analytical insights.

Build and implement AI-powered solutions for the Finance team, including workflow automation, data analysis, system integrations, data pipelines, and supporting infrastructure. Partner with finance stakeholders and engineering teams to translate business needs into technical solutions, prepare and validate data, and guide finance professionals in using AI tools. The role will deliver scalable AI implementations and help establish AI automation best practices.

Leads the design, implementation, deployment, and operational support of enterprise AI and software solutions. Develops production generative AI applications using Python, C#/.NET, Azure AI, and large language models. Oversees architecture, monitoring, incident resolution, change management, documentation, performance optimization, client requirements, and application infrastructure. Provides secondary leadership for technical matrix teams and supports budgeting and enterprise application operations.

Lead the development and deployment of production-ready computer vision and deep learning systems for medical imaging. Responsibilities include model development, end-to-end ML pipelines, validation, data strategy, clinical readiness, explainability, regulatory support, cross-functional collaboration, and technical mentorship. The role requires translating research into reliable, clinically validated AI solutions for real-world healthcare environments.

Analyze equipment telemetry, service history, operating conditions, and environmental data to develop predictive health scores for UPS and thermal systems. Build Python, SQL, and Tableau analytics solutions; validate incidents with field results; refine machine learning models; improve reporting and corrective-action recommendations; and collaborate with engineering, product, field, and customer teams. Apply simulation, FMEA, Six Sigma, and energy-efficiency standards to improve maintenance planning, asset reliability, and operational efficiency. Occasional travel is required.

Leads Central Health System’s data science team and serves as its senior technical authority. Develops and validates predictive, machine learning, NLP, and forecasting models supporting population health, care management, and clinical decision-making. Oversees AI governance, model risk assessment, fairness, reproducibility, documentation, and regulatory alignment. Partners with clinical, data engineering, governance, and analytics teams while mentoring staff and managing advanced analytical standards across Epic, Azure, Snowflake, and other enterprise data sources.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Leads the architecture and delivery of scalable data engineering, AI/ML, and agentic AI solutions. Partners with technical and business stakeholders, oversees implementation quality, conducts code reviews, manages backlogs, timelines, resources, and risks, and coordinates delivery across teams. The role also evaluates emerging technologies, monitors progress, prepares status reports, facilitates meetings, and maintains technical documentation.

Lead end-to-end annotation projects: define workflows, coordinate teams (annotators, reviewers, experts), track progress and quality via dashboards, manage launches and phase transitions, pilot tooling and methodology improvements, and build scalable operational systems and documentation to support annotator community and cross-functional alignment.