Top AI & Machine Learning Jobs

Teach introductory and upper-division Computer Science and Data Science courses, develop curricula, conduct an active research program involving students, pursue external grants, advise students, participate in university service, and contribute to interdisciplinary and community engagement initiatives.

Leads AI-driven sales strategy and commercial systems, oversees sales automation applications, improves quote-to-order processes, analyzes data with AI, implements AI tools, evaluates performance, and advises sales stakeholders on AI solutions.

Supports validation and deployment of Level 4 autonomous driving and robotic systems using generative AI, vision-language-action models, world models, simulation, and machine learning. Analyzes driving data, develops edge-case simulations, contributes to safety and reliability improvements, and communicates technical findings to research teams in the United States and Japan.

Build and own AI-powered internal tools from discovery through deployment, rollout, and ongoing improvements. Develop document analysis, agentic workflows, automation, research, routing, and analytics systems that reduce manual work. Partner with teams across the company to identify opportunities, scope solutions, enable employees to use AI effectively, and support customer-facing analytics and implementations.

Build and optimize production systems for heterogeneous machine learning models, including small models, medium models, and LLMs. Develop distributed systems for large-scale data processing, model training and validation, experimentation, deployment, and CI/CD. Ensure system robustness, scalability, and long-term maintainability while supporting data management and evolving model-development workflows.

Design, test, refine, and roll out AI deployments that extract information from healthcare documents and automate authorization and verification workflows. Analyze model accuracy, translate healthcare processes into technical specifications, create prompt and deployment playbooks, define performance metrics, and collaborate with operations, engineering, and healthcare experts to scale automation strategies.

Build and deploy autonomous AI agents and LLM-powered applications for insurance brokerage workflows. Responsibilities include owning the development lifecycle, creating evaluation frameworks and benchmarks, monitoring production performance, optimizing and scaling systems, mentoring teammates, and promoting AI adoption across the company. Requires production software engineering experience, LLM application expertise, dataset development, benchmark creation, and TypeScript proficiency.

Embed with customers to understand their systems and business needs, then build data pipelines, workflows, and automations that deliver measurable AI-powered outcomes. Serve as a trusted technical partner in ambiguous environments, integrating APIs and applying SQL, Python, data modeling, and agentic systems. Travel to customer sites, rapidly learn new technologies, and help establish Planhat’s Forward Deployment Engineering discipline and playbook.

Leads the design, development, and deployment of production-scale AI and machine learning systems. Builds solutions using LLMs, generative AI, NLP, computer vision, and deep learning; architects ML pipelines; optimizes models through fine-tuning and prompt engineering; translates technical results into business strategies; partners with stakeholders; communicates outcomes; and mentors data scientists while establishing MLOps best practices.

Leads Terrestrial Bio’s enterprise Data & AI strategy, infrastructure, governance, ontology, systems integration, and applied AI roadmap. Builds and manages the Data & AI team, selects MES, ERP, LIMS, ELN, and QMS systems, oversees data pipelines and agentic automations, and ensures GxP-compliant data integrity, auditability, privacy, and change control. Reports to the CEO and communicates strategy, risks, progress, and investment plans to executive leadership and the Board.