Top AI & Machine Learning Jobs

Supports enterprise AI governance by administering risk-tiering and intake workflows, maintaining governance data and documentation, tracking exceptions and remediation, monitoring lifecycle reviews, and developing dashboards and reporting. Partners with business, technology, security, legal, and compliance teams to support audits, improve controls, maintain AI governance policies, and enable responsible AI adoption.

Lead the data science function, owning machine learning strategy, predictive models, healthcare analytics, DBT data marts, and executive dashboards. Analyze claims, eligibility, pharmacy, EMR, HIE, clinical, and operational data to identify high-risk patients, evaluate clinical interventions, and improve patient and provider engagement using AI and LLMs. Collaborate with clinical, operations, and technology teams while leading data science staff in a fast-growing healthcare organization.

Design, develop, test, deploy, and maintain UiPath RPA solutions enhanced with agentic AI. Analyze business processes, gather requirements, document workflows, integrate systems, monitor automated processes, troubleshoot issues, and optimize solutions. Provide user training and support while collaborating with stakeholders and recommending AI and automation best practices.

Own the post-training pipeline for AI roleplay models, transforming raw data into improved model behavior through supervised fine-tuning, reinforcement learning, reranking, data curation, and evaluation. The role involves closing the loop from evaluation signals to training runs, defining problems independently, and working onsite with a small, fast-moving engineering team.

Facilitate adaptive, hands-on AI workshops for startups, scale-ups, and SMBs; teach AI tools, workflows, adoption mindsets, and behavioral change. Maintain fluency across Claude, Gemini, and ChatGPT, develop learning content, support client relationships, contribute to marketing and thought leadership, and help improve Clarinet’s workshop offerings based on participant feedback.

Conduct quantitative analysis, process complex and geospatial data, develop visualizations, and create data-driven insights. Build Python-based data-cleaning and manipulation workflows while working with AI/ML technologies and multiple file formats. Desired work includes NLP document extraction, computer vision, automated collection and reporting, data standardization, and statistical techniques such as classification, regression, clustering, data reduction, and causal modeling.

Own end-to-end development of AI-native healthcare solutions, from exploratory data analysis and model strategy through production deployment and monitoring. Fine-tune multimodal generative models, orchestrate AI agents, define rigorous evaluations, build scalable backend systems, and monitor data drift and model performance. The role requires strong software architecture, cloud infrastructure knowledge, observability, testing, autonomous execution, and technical mentorship in a high-stakes healthcare environment.

Participate in a 12-week data science internship by analyzing business problems, building dashboards and analytical models, using SQL, Python, and Power BI, collaborating with analytics teams, communicating findings, and presenting project outcomes to senior leadership.

Builds reporting, dashboards, data pipelines, forecasting models, predictive analytics, and AI-assisted workflows for executive decision-making. The role integrates data from project, financial, CRM, and operational systems; validates and reconciles analytical outputs; applies statistical methods and large language models; automates manual processes; and communicates actionable insights across departments. It requires ownership of business intelligence, predictive modeling, data governance, and executive-facing analysis.

Develop and deploy computer vision and machine learning models for semantic and instance segmentation, object and damage detection across 2D and 3D data. Responsibilities include training loops, dataset curation, labeling guidelines, evaluations, ablation experiments, model export, inference code, testing, profiling, and production support. Collaborate with research, backend, operations, and labeling teams while documenting experiments and improving model performance.