Top Artificial Intelligence Jobs

Lead the development of AI software components for Capital One, including model training, inference, and system optimization. Collaborate with cross-functional teams to deliver groundbreaking AI solutions, ensuring quality and scalability while mentoring junior team members.

The role involves developing AI foundation models, collaborating with cross-functional teams, and conducting applied research to enhance customer experiences using advanced AI techniques.

As a Lead AI Engineer at Capital One, you will design and develop AI systems, collaborate with cross-functional teams, and optimize AI solutions for performance and scalability.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Lead the design and deployment of AI agents, mentor engineering teams, and drive technical strategy and architecture for scalable AI systems.

Lead design, development, deployment, and optimization of large-scale AI/ML systems (training, inference, orchestration, observability). Collaborate cross-functionally to build responsible, scalable generative AI and agentic platforms, improve performance and cost, and shape long-term ML systems roadmap.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Lead Sonar's product marketing for agentic software development: define positioning and AI narrative, present to customers and events, enable sales, align GTM across functions, track competitive landscape, and manage analyst and PR relationships to drive enterprise adoption of AI-generated code verification.

Leads enterprise Applied AI initiatives across Technology, including GenAI use-case prioritization, onboarding, delivery planning, governance navigation, risk mitigation, reporting, and adoption measurement. Partners with business and technology teams, manages roadmaps, backlogs, dependencies, KPIs, and dashboards, and communicates program health and outcomes to senior leadership. Builds communities of practice and drives process improvements across cross-functional teams using Agile and waterfall methodologies.

Evaluate AI models for dangerous CBRNE-related outputs, designing adversarial prompts, multi-turn attack chains, and threat models. Assess technical accuracy and real-world risk, distinguish open-source information from harmful uplift, score responses using severity rubrics, and document failures. Collaborate with AI researchers and policy teams to improve model safeguards and evaluation frameworks while following strict ethical and operational security protocols.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.

Participate in a two-day hackathon solving nonprofit data problems and network with Data & AI teams. Potential pathway to a 2027 Data & AI summer internship where you would build end-to-end data, analytics, and ML solutions, design data platforms and pipelines, develop production-ready models and dashboards, ensure data governance and compliance, work on agile teams, and deliver data-driven recommendations.

Design, develop, and maintain scalable software and AI-enabled solutions. Lead automation, deployment, testing, debugging, documentation, and technical support. Collaborate with product, design, and engineering teams to deliver AI-driven full‑stack applications and improve development and deployment processes.

Build, integrate, deploy, and operationalize secure AI solutions across enterprise platforms. Responsibilities include prompt engineering, orchestration, RAG implementation, enterprise data integration, monitoring, performance tuning, documentation, and governance compliance. The role partners with IT, Security, Privacy, Data, and business teams to deliver production-ready AI capabilities using technologies such as OpenAI, Anthropic, Microsoft 365, Salesforce, and ServiceNow.

Own the full enterprise sales cycle, from prospecting through close, while building executive relationships and creating pipeline. Develop strategic account plans, navigate complex multi-stakeholder deals, translate technical use cases into business outcomes, and close significant opportunities. Partner with founders, product, and engineering teams to shape messaging, pricing, ideal customer profiles, and go-to-market strategy. Help convert early customer wins into repeatable sales motions in a remote, ambiguous, high-growth environment.

Graduate interns will design, develop, test, and debug software, firmware, embedded systems, validation tools, and AI-enabled engineering workflows. Responsibilities include automation, regression testing, log and performance analysis, root-cause investigation, and collaboration with firmware, software, validation, and hardware teams supporting SSD and storage products.

Own Temporal’s AI ecosystem integrations and partnerships portfolio, including roadmap strategy, partner experiences, reference architectures, positioning, launches, enablement, and adoption. Engage customers and track the AI tooling market to prioritize investments. Collaborate with Engineering, Marketing, Sales, Solutions Architecture, DevRel, and external partners to launch integrations, establish KPIs, and drive ecosystem momentum.

Leads iSpot’s AI engineering roadmap and team, taking generative AI, agentic workflows, retrieval systems, and machine learning capabilities from prototype to reliable production. Owns AI architecture, evaluation, guardrails, cost, latency, and operational performance while partnering with product, data science, and platform teams. The role combines hands-on coding and experimentation with hiring, technical leadership, roadmap planning, responsible AI, and executive communication.

Build and deploy AI agents for finance customers by configuring workflows, tuning prompts and models, and integrating enterprise finance systems. Partner with finance teams to identify high-value use cases, improve onboarding and customer journeys, create usage analytics and health frameworks, and inform go-to-market and expansion strategies. The role requires strong finance and accounting knowledge, SQL proficiency, comfort with customer data and BI tools, and the ability to work independently in a fast-paced startup environment.

Leads enterprise AI transformation for strategic customers from strategy and architecture through implementation, adoption, and business value realization. Defines secure, scalable architectures spanning AI agents, applications, data, integration, identity, governance, and operations. Translates business objectives into delivery strategies, roadmaps, and costed solution approaches; governs proofs of concept and pilots; manages risks and stakeholders; develops reusable architecture assets; builds executive relationships; and mentors multidisciplinary technical teams.

Coordinate technical delivery for GenAI applications across engineering, product, data/AI, platform, security, and vendor teams. Manage milestones, dependencies, risks, Jira backlogs, Confluence documentation, Agile ceremonies, UAT, release readiness, acceptance tracking, and delivery communications. Translate approved requirements into actionable work items and document technical architectures, APIs, data flows, integrations, and processes while escalating blockers and maintaining cross-team visibility.

Own the generation layer of Document Intelligence by designing prompts, schemas, validators, multimodal context pipelines, and quality evaluations. Build production LLM features that transform video, audio, PDFs, images, OCR, transcripts, and keyframes into validated maintenance entities. Balance model quality, cost, and latency, monitor regressions, and collaborate with processing and domain engineering teams.

Owns the scaling of AI and agent go-to-market motions across Microsoft field, partner, and direct customer channels. Responsibilities include diagnosing customer and sales journeys, defining scalable sales programs and offers, identifying asset gaps, enabling partner execution, leading cross-functional strategy programs, and tracking pipeline, adoption, execution, and ROI. The role requires enterprise technology GTM expertise, program leadership, market analysis, and the ability to influence teams without direct authority.

Own end-to-end architectures for cloud, data, machine learning, deep learning, generative AI, and agent-based client solutions. Lead architecture reviews, technical governance, production readiness, and stakeholder engagement while addressing security, compliance, scalability, reliability, observability, and cost. Support pre-sales through scoping, proposals, effort estimates, and presentations. Mentor engineers, develop internal accelerators, and contribute to AI consulting engagements from assessment through deployment.