Top AI & Machine Learning Jobs

Data Science Interns will improve production machine learning models for lifetime value, unit economics, and forecasting. They will audit models, engineer features, validate predictions, design A/B tests with Product, Marketing, and Sales, and interpret results. The role requires rigorous statistical inference, strong Python and SQL skills, clear communication, and a willingness to investigate model and data issues. Interns will work with Python, dbt, Dagster, Redshift, Athena, Cube, and Superset under mentorship.

Leads the engineering team developing Robinhood’s agentic AI platform and applications. Responsibilities include hiring and developing engineers, setting technical direction for orchestration, tool integration, evaluations, permissioning, guardrails, and approval flows. The role owns production reliability, SLOs, incident resolution, auditability, and measurable performance across large-scale nondeterministic systems, while partnering with Legal, Compliance, and Security in a regulated financial environment.

Build and deploy AI-powered tools, workflows, applications, scripts, and integrations for WithCoverage’s Growth team. Partner with Product on implementation, testing, debugging, deployment, and iteration. Join sales and prospect conversations as a technical expert, explain AI concepts to nontechnical operators, demonstrate practical applications, and develop prototypes based on customer workflows. Document solutions, improve reliability, and communicate technical tradeoffs to Product and Engineering.

Leads the end-to-end architecture, deployment, operation, and scaling of AI compute infrastructure for orbital data centers. Owns GPU/CPU, networking, storage, orchestration, cloud-platform, spacecraft network, testing, and qualification strategies. Builds and manages the compute payload engineering organization, leads hardware/software co-design, and makes supplier, component, technology-partner, and make-or-buy decisions. Requires extensive hyperscale, HPC, AI infrastructure, GPU cluster, hardware, networking, storage, and team leadership experience.

Lead development, validation, deployment, and optimization of predictive models and analytics. Partner with claims, underwriting, actuarial teams to transform complex datasets into actionable insights, ensure data quality and compliance, and communicate results through visualizations. Advance data science capabilities across the organization.

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.