Top AI & Machine Learning Jobs

Build and deploy production AI/ML solutions for manufacturing, including predictive maintenance, defect detection, anomaly detection, process optimization, quality prediction, and engineering productivity. Own the full AI lifecycle from data preparation and experimentation through deployment, monitoring, and improvement. Collaborate with manufacturing, quality, equipment, automation, and software teams in an active manufacturing environment.

Design, develop, deploy, and maintain machine learning and AI solutions for clinical care and business operations. Coordinate the machine learning lifecycle, implement CI/CD and model-training practices, analyze large datasets, create visualizations and leadership reports, document models, and translate technical findings for business stakeholders. Lead cross-functional projects involving clinical leaders, data engineers, developers, and administrative teams.

Develops and deploys AI solutions that automate business processes, improve decision-making, and support innovation. Responsibilities include building AI prototypes and applications, tuning models, analyzing data, integrating enterprise systems, developing generative AI and intelligent search solutions, improving data quality, and partnering with business and IT teams. The role also evaluates AI technologies, documents recommendations, and promotes responsible AI practices involving privacy, security, governance, and ethics.

Own the quality and behavior of AI-generated interactive content. Build evaluation systems using production creator data, develop agentic ML systems that turn ideas into playable games, debug failures across models and pipelines, run rigorous experiments, and help establish the AI team’s technical standards and direction.

Conducts healthcare data collection, validation, analysis, reporting, dashboard development, root-cause analysis, program evaluation, predictive modeling, and AI/ML work. Collaborates with stakeholders to define requirements, improve business outcomes, connect complex healthcare data sources, and communicate findings through reports, visualizations, and recommendations. Manages multiple projects while delivering accurate, timely results.

Computer Vision Intern developing detection, segmentation, and classification models for food and material recognition. Responsibilities include curating and QAing image/video datasets, building reproducible training and experiment-tracking pipelines, evaluating models, documenting findings, and collaborating with engineering and data teams to deploy computer vision systems to production devices.

Lead the MLOps engineering team and roadmap supporting large-scale machine learning training and inference. Oversee cloud infrastructure, Kubernetes, distributed systems, observability, data pipelines, and inference modernization. Manage engineers, budgets, and cloud costs while partnering across machine learning, data science, product engineering, and infrastructure. Drive platform scalability, build-versus-buy assessments, stack refreshes, production reliability, and adoption across the organization.

Lead enterprise client discovery, workshops, executive discussions, and solution architecture for Generative AI, RAG, agentic AI, conversational AI, and workflow automation. Translate business needs into measurable AI use cases, guide delivery, ensure security and governance standards, and support presales demonstrations and proofs of concept.

Build and scale machine learning training, inference, and Generative AI systems for personalized text, image, audio, and video content. Define model-generation roadmaps, develop and operate models at scale, prototype innovative solutions, establish testing and fine-tuning processes, and partner with product, design, business, and engineering teams. Drive data-informed product development through feature and company-level metrics.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.