Top Artificial Intelligence Jobs

Own the direction and delivery of user-facing generative AI products, including AI agent and chat experiences. Understand technical and non-technical users, identify workflow opportunities, prioritize product development, and partner with engineering and infrastructure teams to deliver stable, performant products. Collaborate with Learning and Development and business teams to drive adoption, experimentation, and AI skill development. Evaluate product success through data, cost-benefit analysis, and user research while adapting to industry changes.

Own customer deployments of AI products from discovery through launch. Analyze complex customer data, identify discrepancies, resolve blockers, coordinate with customer and internal teams, and partner with Product and Engineering on issue resolution and product improvements. Establish repeatable deployment processes, documentation, and communication practices while managing risks, decisions, and progress across stakeholders.

Build and scale developer platform capabilities, AI-powered coding and review tools, CI/CD systems, Kubernetes infrastructure, and self-service developer workflows. Improve reliability, security, observability, and delivery speed through automation, reusable templates, guardrails, documentation, and incident-response practices. Collaborate closely with platform teammates, contribute to roadmap decisions, and communicate clearly about progress, blockers, and technical tradeoffs.

People-leadership role managing and growing a team of data scientists and AI engineers building retrieval, extraction, and reasoning systems for scientific discovery products. Focus on hiring, coaching, performance management, team health, cross-team initiatives, and communicating work to stakeholders. Maintain technical fluency in LLMs, RAG, agentic workflows, and evaluation to mentor teams, while partnering with technical leads and product owners.

Support development of an autonomous materials-testing platform integrating nanoindentation, tensile testing, DIC, and EBSD. Develop Bayesian optimization and Gaussian process models to select experimental conditions, and apply active learning and CNNs to interpret real-time nanoindentation and DIC data. The internship is conducted onsite at Idaho National Laboratory on a 4x10 schedule.

Leads an AI organization supporting Long Term Care by developing and scaling production AI, machine learning, and GenAI solutions. Manages AI engineers and data scientists, owns initiatives from business definition through deployment and value measurement, and guides MLOps, cloud architecture, governance, monitoring, and responsible AI practices. Partners with executives and cross-functional stakeholders to identify opportunities, communicate impact, drive adoption, and build high-performing technical teams.

Own global compensation cycle administration, system configuration, benchmarking, job architecture, salary structures, pay reporting, equity support, and compensation analysis across the Americas, EMEA, and APAC. Partner with HR, recruiting, payroll, legal, finance, and managers on offers, promotions, pay equity, and transparency requirements. Improve compensation operations through AI and automation, maintain data integrity, and deliver training and communications on compensation programs.

Own the strategy, roadmap, and execution of AI-powered conversational products across ChatGPT, WhatsApp, RCS, and other platforms. Lead MCP and API integrations, LLM-powered homeowner qualification and contractor matching, messaging journeys, compliance, CRM connectivity, product analytics, and zero-to-one launches. Partner with Engineering, Legal, Marketing, and integration teams to deliver scalable, revenue-generating customer acquisition experiences.

Own security end to end as AIUC’s first security hire, securing its product, agent evaluation platform, infrastructure, corporate systems, vendors, data, and customer environments. Build sandboxing, credential isolation, least-privilege access, detection, logging, incident response, and automated security controls. Investigate emerging agent attacks, turn research into production defenses, support customer security reviews, and contribute findings to AIUC-1 standards and its CISO consortium.

Serve as Lithos’s first marketer, owning positioning, messaging, audience discovery, technical content, founder visibility, launches, events, sales enablement, and marketing measurement. The role requires translating AI inference benchmarks and customer outcomes into compelling stories for developers and engineering leaders. This hands-on position suits someone comfortable building a marketing function from scratch at an early-stage startup, collaborating closely with technical founders, engineers, customers, partners, and investors.

Leads engineering strategy, teams, and delivery for web application and AI attack capabilities in a cybersecurity SaaS platform. Oversees application discovery, crawling, browser automation, authentication testing, AI/LLM security, attack automation, production reliability, roadmap execution, organizational development, hiring, and cross-functional collaboration. Establishes quality, observability, testing, and operational standards while translating offensive security research into dependable customer-facing products.

Own strategy and roadmap for Crunchbase's customer-facing AI data layer. Identify and validate model-derived data products, lead cross-functional development, set quality standards, drive adoption and monetization, and monitor performance and product economics.

Build and improve AI evaluation systems, including LLM judges, production tracing, goldsets, benchmarks, data pipelines, and agentic feedback loops. Partner with data and ML engineers to improve evaluation accuracy, cost, runtime, and reliability. Contribute to architecture and code reviews while mentoring engineers and communicating evaluation results to teams.

Staff Software Engineer responsible for architecting reliable internal platforms, integrating legacy and SaaS systems, and applying production-grade generative AI and agentic systems. The role sets technical direction across teams, leads build-versus-buy decisions, improves reliability and security, designs analytics-ready data models, evaluates AI opportunities, manages AI costs and ROI, and mentors engineers while advising leadership.

Leads the design, development, deployment, and operation of scalable AI solutions, including generative AI, agentic workflows, RAG pipelines, MCP integrations, workflow automations, and evaluation systems. Builds production software across a polyglot technology stack, establishes testing and observability practices, mentors engineers, influences technical direction, and partners cross-functionally to deliver secure, maintainable, responsible AI applications.

Designs, deploys, and maintains AI and machine learning solutions, including generative AI, LLM, RAG, and conversational AI applications. Owns agentic engineering infrastructure, application guardrails for patient-care workflows, data pipelines, analytics, model governance, monitoring, and cloud deployment. Provides architectural leadership, improves engineering throughput, ensures privacy and compliance, and translates business needs into scalable AI solutions.

Build and operate Node.js and TypeScript backend services using durable workflows and AI-assisted development. Define service patterns, orchestrate migrations, maintain MCP and AI workflow tooling, and improve observability through metrics, alerts, dashboards, and tracing. Handle production incidents, contribute targeted frontend changes in React and Angular, and collaborate on architecture and code reviews in a fully remote environment.

Coordinates AI and data governance across Innovation, IT, Security, Privacy, Legal, and business teams. Leads information governance assessments, evidence collection, remediation tracking, policy updates, data lifecycle standards, stewardship, privacy, retention, access, and DLP requirements. Maintains AI inventory and governance records, translates requirements into platform workflows, reports risks and progress, manages staff, and supports responsible AI adoption and compliance reviews.

Conduct deep AI and quantitative research for electronic trading by pre-training Transformer and time-series foundation models on large-scale market data. Develop representations, objectives, architectures, distributed training methods, evaluation protocols, and model-serving components. Fine-tune models for alpha generation, pricing, market making, execution, and risk management while studying scaling laws, transferability, robustness, data efficiency, latency, and economically meaningful trading outcomes.

Lead analytics, AI, and machine learning strategy and delivery across Payments, including scalable data products, production deployment, governance, model lifecycle management, and Generative AI initiatives. Partner with business, Quantitative Research, Technology, Product, and enterprise AI teams to optimize deposits, liquidity, balance sheets, and revenue. Drive self-service analytics, AI adoption, stakeholder alignment, regulatory compliance, and measurable business outcomes.

Leads the strategy, governance, adoption, and continuous improvement of Production Management tools supporting enterprise application and infrastructure services. Oversees observability, incident, problem, change, event, resilience, reporting, workflow, and risk-management capabilities. Drives responsible adoption of Claude Code, GitHub Copilot, NowAI, and other AI solutions to improve operational insight, automation, service stability, and incident response. Partners with technology owners and senior stakeholders to define roadmaps, metrics, integrations, controls, and continuous-improvement initiatives.

Architect and build reliable, scalable AI platform services supporting model training and inference across hybrid-cloud environments. Develop reusable APIs, SDKs, tooling, and reference architectures; establish observability, resilience, security, automation, and cost-optimization standards. Partner with engineering and product teams to deliver platform capabilities, meet reliability targets, resolve production incidents, and drive remediation. Mentor engineers and promote responsible, secure use of AI-assisted development tools.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Leads data science and machine learning initiatives addressing cybersecurity and technology risk challenges. Responsibilities include exploratory data analysis, statistical and deep learning model development, feature engineering, secure production coding, model governance, AI workflow guardrails, and stakeholder communication. The role also supports security control effectiveness, production model monitoring, and responsible deployment of AI solutions using enterprise-authorized capabilities.