Top AI & Machine Learning Jobs

Develop machine learning models and advanced analytics solutions across the full model lifecycle, including data exploration, feature engineering, validation, refinement, and operational monitoring. Build AI capabilities for intelligent products and data-driven decisions while monitoring system health, user metrics, performance, and availability. Collaborate across teams, mentor technical staff, optimize processes, and apply secure software development practices.

Analyze operational, financial, HR, and job-management data; build dashboards, predictive models, and business forecasts; identify and implement AI and automation opportunities; integrate AI tools through APIs and automation platforms; measure productivity and KPI improvements; support tool adoption and training; and ensure responsible data and AI practices.

Leads enterprise AI strategy, roadmaps, and project portfolios from ideation through deployment. Manages AI scientists and engineers, partners with executives and stakeholders, translates business needs into technical requirements, and drives AI adoption, governance, education, and measurable business impact. Oversees solutions using analytics, machine learning, generative AI, data engineering, and related technologies while establishing success metrics and ensuring timely, budget-conscious delivery.

Conduct research and develop machine learning, generative AI, stochastic simulation, and optimization solutions for clinical trial operations. Build predictive models, optimization engines, LLM workflows, and data pipelines supporting enrollment forecasting, cost optimization, site selection, document processing, and risk management. Validate models, promote responsible AI and data quality, contribute reusable code and documentation, and communicate findings to technical and clinical stakeholders.

Build and launch AI agents for go-to-market teams across marketing, sales, product, and revenue operations. Responsibilities include designing prompts and agent personas, integrating LLMs with GTM systems, deploying and monitoring production agents, improving retrieval and model performance, documenting workflows, enabling users through training, and ensuring compliance with security, legal, and AI standards.

Lead and manage a team developing and operationalizing machine learning systems for structured and unstructured clinical data (EHR, text, imaging). Define technical strategy, oversee model lifecycle management, fine-tune and deploy foundation models, ensure data quality and model governance, partner cross-functionally, and mentor ML scientists and technical leaders.

Builds firmwide AI infrastructure and production-grade generative AI applications, including agentic workflows using RAG, semantic search, planning, and structured outputs. The role develops evaluation harnesses, guardrails, approval workflows, tool-use controls, source attribution, and kill switches while optimizing reasoning quality, retrieval accuracy, latency, cost, safety, and user experience. It also evaluates foundational and open-weight models for enterprise use.

Develops and supports secure AI-enabled applications and data integrations for a law enforcement agency. Responsibilities include training and optimizing machine learning models, integrating LLMs and agentic systems into on-premises applications, preprocessing structured and unstructured data, building RAG workflows, evaluating models, and ensuring CJIS and PII compliance. The role also involves collaboration, code reviews, testing, deployment, prototyping emerging AI tools, and maintaining data governance.

Leads enterprise AI strategy, roadmap development, and delivery of AI-powered solutions. Manages AI scientists and engineers, translates business needs into technical requirements, oversees AI project portfolios, and drives adoption, governance, education, and measurable business impact. Collaborates with executives, stakeholders, data scientists, and engineering teams to develop scalable analytics, machine learning, and generative AI solutions.

Develop and deploy production-grade 3D LiDAR and computer-vision perception systems for mobile robots. Own algorithms from sensor characterization through fleet deployment, while building AI agents and automated pipelines for data labeling, dataset curation, edge-case triage, and regression validation. Collaborate across controls and software teams, lead architecture and testing efforts, mentor engineers, and improve collision avoidance and autonomous behaviors across diverse operating environments.