Top Artificial Intelligence Jobs

Build, deploy, operate, and improve production AI systems for enterprise customers. Responsibilities include designing compound AI workflows using models, prompts, agents, tools, retrieval, evaluation, and integrations; developing Python services; monitoring reliability; debugging and iterating on live systems; creating evaluation and observability frameworks; and collaborating directly with French-speaking customers and subject matter experts.

Build and maintain AI-accelerated automated testing across web, API, and mobile using Playwright. Integrate quality gates into GitHub Actions, expand functional, regression, API, and performance coverage, reduce flaky tests, and perform exploratory testing. Evaluate AI-generated tests and non-deterministic AI features, track quality metrics, convert escaped defects into regression tests, and collaborate with engineering teams. Mentor QA engineers and improve testing processes across the organization.

Design, train, evaluate, and optimize machine learning models for wireless network use cases and real-time edge inference. Build telemetry data pipelines, deploy containerized models on Kubernetes and GPU infrastructure, develop AI-driven network applications, and implement MLOps practices. Benchmark resource contention, ensure model security and provenance, document results, and collaborate with wireless, RF, cybersecurity, and government stakeholders. The role requires hands-on lab work, technical communication, and eligibility for a Secret clearance.

Leads 3M’s enterprise generative AI adoption strategy and AI employee experience. Owns product roadmaps, use-case prioritization, governance, executive engagement, AI champion communities, platform integration, adoption measurement, and value realization. Partners with business, technology, data, cybersecurity, legal, privacy, and executive teams to move AI solutions from pilots to secure, scalable production deployments and measurable business outcomes.

Designs enterprise-scale AI solutions on shared platforms, including agentic systems, RAG pipelines, knowledge services, and orchestration. Translates business needs into reusable architecture patterns, leads design reviews, and establishes standards for secure, governed, cost-efficient AI systems. Partners with product, governance, platform, and engineering teams to move solutions from architecture through production while promoting interoperability and platform reuse.

Leads cross-functional enterprise AI platform programs spanning governance, operations, adoption, change management, lifecycle management, and FinOps. Establishes responsible AI standards, operating models, roadmaps, metrics, intake processes, and governance forums. Partners with technical, security, legal, data, engineering, and business stakeholders to improve platform reliability, user experience, cost transparency, compliance, and adoption across global teams.

Leads the strategy, governance, and end-to-end delivery of AI, machine learning, and automation programs across Global Operations. Translates operational challenges into technical requirements, coordinates data, security, platform, and delivery teams, manages risks and dependencies, and oversees deployment, adoption, operational handoff, monitoring, and value realization. The role partners with supply chain, manufacturing, logistics, and other operations stakeholders to scale AI-enabled solutions.

Leads end-to-end AI, machine learning, and automation programs across Global Operations, from use-case discovery and solution design through deployment, adoption, operational handoff, and value realization. Translates business needs into technical requirements, coordinates data, security, platform, and delivery teams, manages roadmap and program health, and ensures solutions are embedded in operational workflows with measurable outcomes.

Six-month engineering internship on the SABRE Process Development team, applying data analytics, machine learning, automation, computer vision, predictive modeling, digital twins, and sensor integration to improve semiconductor electroplating equipment and processes. The intern will analyze experimental data, develop intelligent equipment capabilities, explore tool design concepts, and optimize manufacturing processes while working across hardware, software, and engineering disciplines.

Develops and maintains enterprise dashboards, scorecards, reporting solutions, data models, and analytics for leadership and business stakeholders. Analyzes data from IWMS, EHS, SAP, and other systems; monitors data quality; identifies trends and risks; and supports AI, automation, predictive analytics, and digital transformation initiatives. Partners with cross-functional teams, gathers requirements, trains users, governs reporting standards, and recommends improvements to operational performance and decision-making.

Leads the design, development, and deployment of AI-driven CAD automation and engineering applications. Builds NX and Creo customizations, integrates CAD with PLM systems, develops data pipelines, and applies machine learning, generative AI, and optimization to design, validation, routing, configuration, and change-management workflows. Serves as an AI/CAD subject matter expert, establishes technical roadmaps, mentors engineers, and drives production adoption of intelligent engineering tools.

Lead complex enterprise AI and automation programs across applications, data, infrastructure, and information security. Responsibilities include defining scopes, roadmaps, budgets, schedules, governance, and success metrics; coordinating stakeholders, vendors, and delivery teams; managing risks, dependencies, and executive communications; ensuring data, security, privacy, compliance, and operational readiness; and tracking adoption and business value. The role also develops repeatable AI delivery playbooks and governance practices.

Lead full-cycle recruiting for high-impact Physical AI roles across autonomy, machine learning, perception, simulation, AI infrastructure, controls, and hardware engineering. Advise senior engineering leaders on talent strategy, market intelligence, search design, engagement, and closing. Build relationships with AI, robotics, and academic talent communities; use data and AI-enabled recruiting tools to improve hiring; establish strategy for the Physical AI market; and mentor recruiters and sourcers.

Build and maintain conversational AI agents and sales intelligence data products for revenue, pipeline, bookings, discounting, and territory analytics. Integrate enterprise systems including Salesforce, Snowflake, Databricks, BigQuery, and SharePoint; develop semantic models, retrieval pipelines, vector indexes, and evaluation datasets. Partner with sales, finance, IT, and security teams to scope solutions, validate quality, support governance, document capabilities, and enable user adoption.

Designs and owns enterprise software and AI systems, including full-stack applications, offline-first mobile tools, data platforms, financial models, LLM applications, machine learning workflows, and integrations with enterprise systems. Responsibilities include architecture, data modeling, evaluation, production reliability, security, migrations, observability, risk mitigation, and mentoring. The role partners with operations, finance, HR, safety, and project teams to deliver auditable, scalable solutions.

Build and lead Intelligent Operations for production AI solutions. Own handover standards, monitoring, alerting, incident response, SLA compliance, AI quality assurance, drift detection, benchmarking, guardrail validation, retraining triggers, security, compliance, and scalable operational tooling. Mentor engineers, establish technical standards, develop self-healing capabilities, and collaborate globally with delivery teams, platform engineers, customers, and partners.

Leads enterprise data, analytics, and AI strategy and delivery from ideation through adoption. Owns product and service roadmaps, strategic program outcomes, emerging technology scaling, governance, security, and architecture decisions. Builds and mentors cross-functional teams across engineering, product, solution delivery, and analytics while partnering with senior stakeholders to prioritize investments and drive measurable business impact.

Build and operate a scalable AI security platform, including infrastructure, APIs, SDKs, distributed systems, and security primitives. Design identity, authentication, authorization, encryption, data protection, and AI-agent safety controls. Partner with security and engineering teams to create reusable platform capabilities, improve reliability and developer experience, and shape the architecture of Robinhood’s AI Security platform.

Own a portfolio of property management customers, driving retention, satisfaction, adoption, and expansion. Serve as the primary customer contact, identify churn risks using AI-powered health signals, conduct business reviews, support platform configuration, promote AI features, and coordinate with Sales, Support, Professional Services, Product, and Engineering. Guide executive stakeholders, communicate technical capabilities clearly, resolve escalations, and deliver customer value throughout the success journey.

Lead enterprise-wide communications, public affairs, public relations, government relations, crisis communications, strategic events, visual brand management, internal communications, and stakeholder engagement. Serve as company spokesperson, advise senior leadership, shape global multi-brand communication strategies, and lead a team of approximately 25 professionals. The role requires aligning communications with business objectives, managing reputation, and advancing the company’s public image.

Own the strategy, roadmap, and commercial success of an AI SOC platform supporting MDR, MXDR, managed network security, and managed cloud security. Drive AI-assisted triage, automation, integrations, APIs, multi-tenancy, partner enablement, and MSSP-scale service packaging. Define network and cloud security capabilities, governance requirements, competitive positioning, pricing, and business cases while presenting to customers, partners, sales teams, and executives. Measure automation, workload reduction, cost-to-serve, adoption, retention, and partner revenue.

Leads enterprise technology risk management across cloud, cybersecurity, data protection, AI, and third-party ecosystems. Oversees risk assessments, regulatory compliance, SOX ITGC controls, issue remediation, risk reporting, automation, and emerging technology governance. Partners with technology, business, audit, and regulatory stakeholders while developing a high-performing risk management team.

Own complex AI programs from concept through model development, cloud and edge deployment, and post-launch optimization. Lead cross-functional teams across AI research, engineering, product, and hardware; manage roadmaps, dependencies, metrics, executive reporting, data lifecycles, annotation pipelines, evaluations, and vendors. The role also mentors TPMs and supports AI enablement. Experience with large-scale distributed systems, machine learning delivery, cloud platforms, technical documentation, and ambiguous requirements is required.

Leads investor experience strategy and cross-functional initiatives for a fund administration platform. Analyzes complex investor data, develops dashboards and management reporting, identifies servicing improvements, and coordinates stakeholders across Investor Services, AML, tax, documentation, operations, and technology. The role manages projects from concept through implementation, improves onboarding and servicing workflows, supports strategic investors, and translates operational insights into scalable, compliant solutions.

Leads end-to-end data science projects for automated underwriting and related insurance analytics. Develops statistical and machine-learning models, evaluates performance, analyzes complex datasets, and implements scalable production solutions. Partners with underwriting, product, business, engineering, and technology teams; monitors models; documents governance and validation processes; evaluates external data and third-party solutions; provides technical direction; reviews work; and mentors other data scientists.