Top AI & Machine Learning Jobs

Executes ETL pipelines, data ingestion, analytics production runs, and machine learning inference operations for a government supply chain program. Maintains pipeline automation using Python, SQL, and orchestration tools; monitors model performance, validates data quality, produces Red Team and Blue Team analytics, documents workflows, and supports configuration releases under technical oversight.

Research Scientist developing and scaling reinforcement learning methods for frontier scientific language models. Responsibilities include designing compute-scaling experiments, improving policy optimization and credit assignment, developing adaptive sampling and curriculum strategies, studying training bias and stability, and improving training and inference efficiency. The role requires implementing and testing research ideas across complex training systems and transferring small-scale findings to large-scale runs.

Assist with AI platform support, user onboarding, troubleshooting, training, documentation, responsible AI governance, and adoption reporting. The apprentice will learn from senior team members while collaborating with business and technical teams across the organization. Success requires curiosity, service orientation, communication skills, reliability, learning agility, and foundational comfort with productivity and collaboration tools.

Develop machine learning and data science solutions for financial crime risk management. Responsibilities include analyzing large datasets with SQL, Python, and PySpark; designing ETL pipelines; training supervised and unsupervised models; implementing solutions in Scala; applying LLMs and NLP; supporting customer proofs of concept; and collaborating with product and platform teams in an agile environment.

The Senior Data Scientist develops and operationalizes predictive models and AI algorithms across investment platforms. Responsibilities include data analysis, feature engineering, model experimentation, deployment with MLflow and Docker, CI/CD automation, monitoring for drift and performance, data pipeline and lake design, governance, security, compliance, and stakeholder collaboration. The role requires translating technical findings into strategic recommendations and managing the full machine-learning lifecycle.

Conduct applied AI and psychometric research evaluating the reliability, validity, and fairness of educational products. Analyze large-scale data, apply methods such as IRT and generalizability theory, collaborate with internal and external researchers, and communicate findings through technical documents, presentations, publications, and advisory committee materials.

Leads and codes production AI systems across drug discovery, clinical development, manufacturing, and business operations. Builds generative AI applications, shared AI platforms, model and data pipelines, evaluation frameworks, MLOps processes, and secure regulated workflows. Leads engineers while remaining hands-on in architecture, coding, deployment, troubleshooting, vendor evaluation, and cross-functional collaboration with scientific, clinical, product, security, and business teams.

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.