Top AI & Machine Learning Jobs

Lead the architecture, development, deployment, and scaling of AI-native solutions for federal clients. Build full-stack applications, RAG systems, semantic search, agentic workflows, and reliable data pipelines across structured and unstructured data. Guide embedded engineering teams, integrate solutions into legacy and classified environments, evaluate model performance, uphold responsible AI practices, and serve as the technical lead in client engagements. Travel may reach 100% based on mission needs.

Develop, deploy, and maintain AI and machine learning solutions for municipal operations. Responsibilities include building ML pipelines and NLP models, preparing data, feature engineering, model evaluation, production monitoring, troubleshooting, documentation, and staff training. The role also supports responsible AI, data governance, enterprise system integration, and communication with nontechnical stakeholders.

Embed with business teams to diagnose workflows, lead discovery, design/process redesign, and deliver production AI, automation, integration, and data solutions. Own projects end-to-end from discovery through implementation, adoption, and measurement, developing reusable practices and ensuring security, governance, and measurable business outcomes.

Conduct original interpretability research with customers, analyze advanced AI models, prototype techniques to visualize and manipulate model structures, and translate research into production-ready tools. Lead high-stakes project delivery by defining hypotheses, sourcing data, building tools, and iterating with engineering and customer partners. Help shape the company’s research direction while communicating complex technical concepts clearly.

Leads an engineering team developing SmartDesk, an AI-powered front desk assistant for wellness businesses. Responsibilities include team hiring and development, technical vision and strategy, architecture, product delivery, cross-functional collaboration, resource prioritization, and execution through ambiguity. The manager will turn emerging AI capabilities into reliable, scalable production systems while balancing innovation, quality, and long-term flexibility.

Own scalable backend services and full-stack features for Apartment List’s property-listing platform. Design Kotlin and Ruby services, build TypeScript and React interfaces, make architectural decisions, improve reliability and performance, and collaborate with product, design, and analytics. The role includes mentoring engineers, conducting code reviews, shaping technical roadmaps, and solving ambiguous technical problems involving APIs, databases, distributed systems, and marketplace experiences.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, and Amazon SageMaker. Responsibilities include deployment, monitoring, observability, automation, governance, model lifecycle management, incident response, root cause analysis, CI/CD, infrastructure as code, and production reliability. The role provides technical leadership and mentorship while partnering with data, infrastructure, security, architecture, product, and engineering teams to support cloud-based AI workloads and Generative AI adoption.

Works with enterprise clients to drive AI adoption by assessing readiness, identifying use cases, building adoption roadmaps, delivering training, and managing communications and champion programs. Develops prompt guides, AI-enabled workflows, dashboards, and adoption metrics. Facilitates workshops and office hours, supports responsible AI and safety practices, maintains risk registers, and creates reusable resources to scale and sustain AI usage.

Conduct AI and machine learning research through experiments, model evaluation, prototyping, quantitative analysis, and technical documentation. Investigate retrieval and knowledge systems, emerging models, and AI-enabled products while collaborating with researchers and engineers to translate findings into real systems.

Develops and supports computational solutions for faculty research using HPC, AI, cloud, and GPU technologies. Responsibilities include optimizing scientific software and workflows, advising researchers, supporting machine learning tools and model serving, troubleshooting HPC applications, contributing to grant proposals, evaluating emerging technologies, creating documentation and training, and improving research computing services. The role requires close collaboration with multidisciplinary research teams in a hybrid environment.