Top AI & Machine Learning Jobs

Designs and deploys AI-enabled decision workflows for Department of Defense and federal customers. Responsibilities include configuring data models, dashboards, what-if scenarios, AI agents, and automated workflows; integrating and transforming data; troubleshooting customer issues; advising end users; supporting adoption; and collaborating with product and development teams. The role combines solutions engineering, analytics, customer advisory, and agentic AI workflow design across enterprise platforms.

Join Arootah's advisor network to provide fractional Chief AI Officer / AI Strategy leadership to hedge funds, private equity firms, and family offices. Advise on AI initiatives, strategy, governance, vendor evaluation, workflow automation, change management, and implementation roadmaps. Engage on project-based assignments; client origination handled by Arootah.

Conduct CBRN operational impact analyses, evaluate scientific test results, develop hazard prediction and sortie-generation models, and create custom software and visualization tools. Support Air Force policy, doctrine, research, and strategic planning through technical reports, recommendations, briefings, and collaboration with defense, academic, and research communities.

Architect and build backend systems powering conversational AI experiences. Lead complex projects across the software development lifecycle, define technical direction, design scalable solutions, and ensure system quality. Mentor engineers through code reviews, knowledge sharing, and onboarding while collaborating cross-functionally on product strategy, experiments, and high-value initiatives. Develop LLM-driven infrastructure and maintain operational excellence across production systems.

Conduct independent biomedical AI research to personalize cancer treatment using clinical, genomic, and molecular data. Develop statistical models, machine learning and deep learning pipelines, predictive methods, R packages, and Shiny applications. Validate computational approaches and collaborate with clinicians, researchers, and industry partners on translational studies involving treatment outcomes, synthetic control cohorts, patient similars, and digital twins.

Build and operate AI-driven customer acquisition infrastructure, including outbound workflows, enrichment pipelines, lead routing, personalization, audience scoring, campaign automation, experimentation, and attribution dashboards. Partner with demand generation, lifecycle marketing, data analytics, product marketing, marketing operations, and sales to translate growth objectives into scalable automated systems.

Develop and deploy computer vision modules and machine learning models within AI pipelines. Integrate models with data and software engineering teams, support prototypes, automate data pipelines, build internal tooling, and monitor production performance. The role also involves optimization, infrastructure and security support, staying current with computer vision advancements, and collaborating cross-functionally.

Build and iterate full-stack systems powering AI voice agents from concept through deployment. Research agent orchestration, prompting, and model-alignment approaches; collaborate with product, design, and go-to-market teams to improve usability; document findings and participate in technical reviews. The role also involves debugging live systems, responding to customer needs, and shipping continuous improvements.

Build and improve a scan-to-BIM pipeline that converts point-cloud capture data into accurate, structured 3D BIM models. Responsibilities include model creation, mesh generation, data structuring, IFC and 3D-tool usage, and applying computer vision to automate 3D reconstruction. The role requires construction knowledge and hands-on experience with detailed 3D projects, with occasional quarterly onsite gatherings in San Francisco.

Develop, fine-tune, evaluate, optimize, and deploy large language and multimodal models in local, on-premises, edge, and restricted environments. Build scalable training and inference pipelines, integrate models into production APIs, monitor performance and resource usage, and ensure security and compliance. Collaborate with engineering and business teams, define KPIs, document workflows, evaluate emerging AI techniques, and mentor other engineers.