Top AI & Machine Learning Jobs

Build and execute AI-powered workflows for scripted content, leading projects from concept through delivery. Design scalable production pipelines, identify AI integration opportunities, collaborate with R&D and technical teams, and oversee field, post-production, and delivery operations. Manage cross-functional teams, vendors, and production partners while solving risks and adapting processes in a fast-moving environment.

Entry-level role supporting the evaluation, development, and rollout of generative AI solutions across business teams. Responsibilities include building prompts, datasets, documentation, prototypes, and AI-driven automations; testing and optimizing AI tools; researching emerging technologies; evaluating workflows; and supporting organizational adoption. The position involves close collaboration with product, engineering, and business stakeholders and offers hands-on learning in AI implementation.

Manages data-centric operations research and analytical programs supporting national security and space defense. Leads fact-based studies, requirements analysis, system modeling, data fusion technology development, project execution, resource allocation, and delivery of decision-support solutions to senior Department of Defense leaders. Mentors multidisciplinary teams and oversees complex systems-of-systems and enterprise architecture efforts.

Lead a team of AI/ML, backend, and platform engineers building payment-routing optimization and LLM-powered products. Guide ML lifecycle and AI workflow architecture, oversee low-latency inference services, and maintain AWS, CI/CD, observability, and PCI-DSS standards. Own delivery planning, technical quality, hiring, coaching, and team development while partnering with product, operations, modeling leadership, and executives.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Conduct applied AI safety research using white-box methods such as activation explainers, steering, attribution, and model-internal interventions. Evaluate methods on realistic, large-scale models and agentic coding tasks, develop practical monitors and interventions, publish findings, propose research directions, and engage with researchers, frontier AI developers, governments, and the broader AI alignment community.

Work with strategic enterprise customers to identify high-value AI opportunities, design and prototype AI-powered procurement solutions, demonstrate business value, and drive implementation, adoption, and measurable outcomes. The role combines customer workshops, solution architecture, stakeholder presentations, automation, and domain expertise across procurement, finance, and supply chain.

Build statistical and machine learning models that improve content discovery, user engagement, and retention. Apply causal inference, experimental design, and A/B testing to measure product impact and validate changes. Collaborate with ML engineers to deploy models, translate analysis into product decisions, and develop features using text, multimodal embeddings, and GenAI. Analyze large-scale data and recommender systems to drive measurable improvements in a consumer product serving millions of users.

Develops and supports enterprise generative AI agents, data integrations, automation, reporting, and analytics solutions. Responsibilities include building AI workflows and retrieval-augmented generation solutions, maintaining data quality and governance, managing metadata and lineage, developing integration pipelines and APIs, troubleshooting operational issues, and creating Power BI dashboards. The role translates business needs into technical solutions and promotes responsible AI practices across student success initiatives.

Build Preql’s core agentic AI workflows for financial data cleaning and transformation. Responsibilities include developing LLM-powered data pipelines, conversational interfaces, audit-ready transformation engines, APIs, financial data integrations, and web-based workflow builders. The role owns the full stack, collaborates directly with enterprise customers, and takes the product from proof of concept to MVP while ensuring data lineage, reproducibility, and compliance.