Top AI & Machine Learning Jobs

Lead homeowners insurance loss-cost modeling, including frequency, severity, demand, retention, conversion, and aggregation models. Design and deploy predictive models using Python, SQL, and Airflow-based workflows, ensuring testing, validation, reproducibility, and maintainability. Partner with product, underwriting, and engineering teams to translate actuarial insights into pricing and portfolio decisions while developing reusable analytical tooling and supporting team standards.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Develop scalable operations research and data science solutions using optimization, simulation, machine learning, and information retrieval. Partner with technical and business teams to frame problems, build production-ready analytical models, evaluate performance, recommend technical roadmaps, and deliver actionable insights. Lead data science practices, communicate recommendations, improve model quality, and establish long-term processes, frameworks, tools, and standards.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Develop and maintain MDLinx software features, contribute to system design and code reviews, apply testing and software engineering practices, and troubleshoot production issues. The role emphasizes critically reviewing AI-generated code, identifying opportunities for AI-assisted automation, and supporting application performance, security, and compliance. Engineers collaborate across technical and business teams while expanding ownership and learning the pharmaceutical and media domains.

Leads enterprise-wide AI and engineering transformation as a distinguished technical individual contributor. Drives adoption of AI-powered product capabilities, agentic customer experiences, AI-assisted development, and modern engineering practices. Provides architectural leadership, evaluates emerging technologies, mentors teams, aligns senior stakeholders, and leads complex initiatives through influence across platform, product, AI/ML, and application engineering organizations.

Own the end-to-end machine learning lifecycle, including data preparation, dbt pipelines, feature engineering, model development, deployment, monitoring, and evaluation. Apply ML to risk assessment, cost prediction, image analysis, and business operations. Contribute to architecture and strategy while partnering with Engineering, Product, Operations, and Business teams. Build scalable solutions using Python, AWS, Kafka, Django or FastAPI, PostgreSQL, and GitHub Actions.

Own an end-to-end machine learning project addressing credit, fraud, growth, or product challenges. Responsibilities include data exploration, feature engineering, model training, benchmarking, deployment, monitoring, LLM application, experimentation, and collaboration with engineering, product, and business stakeholders.

Build AI-enabled applications, automations, integrations, data workflows, and prototypes across Salesforce, MuleSoft, .NET, RPA, and enterprise platforms. Partner with business and IT stakeholders to identify opportunities, validate measurable value, assess AI and architectural risks, and deliver supportable solutions. Document designs, dependencies, prompts, limitations, runbooks, and handoffs while contributing to exploratory engagements and recommending pivots when initiatives lack value.

Supports the design, testing, calibration, and deployment of predictive risk models for Naval safety. Analyzes complex datasets using time series analysis, forecasting, statistical modeling, causal inference, anomaly detection, feature engineering, and machine learning. Develops models in Python or R and collaborates with data scientists and software engineers to transition analytical solutions into operational environments.