Top Artificial Intelligence Jobs

Own and expand customer relationships across the Western United States for a cloud-based healthcare genomics platform. Drive adoption, usage, and account growth within hospitals, laboratories, and healthcare organizations; identify expansion opportunities; navigate complex stakeholder groups; provide account support; and partner with sales teams on customer meetings and commercial development. The field-based role requires approximately 30–40% territory travel.

AI Enablement Engineer who helps non-engineering Builders turn AI prototypes into secure, scalable, tested, observable, and maintainable production applications. Responsibilities include architecture guidance, technical coaching, code and security reviews, deployment, reusable templates and guardrails, production support models, and occasional end-to-end strategic builds. The role partners with engineering, security, and data teams while advancing AI application standards and Builder self-sufficiency.

Leads enterprise AI adoption by establishing governance, standards, intake, portfolio management, security controls, evaluation practices, and enablement patterns. Partners with Legal, Privacy, Security, Technology, FinOps, and business teams to identify automation opportunities, manage AI platforms and use cases, monitor value and risk, and support scalable solution delivery. Builds and develops a geographically distributed AI Enablement team while evaluating emerging technologies and improving enterprise AI capabilities.

Lead a small engineering team while remaining hands-on in production coding. Deliver scalable product features, make architecture decisions with the CTO, apply AI-assisted development workflows, mentor engineers, and contribute to machine learning and product roadmaps. The role requires full-stack expertise with backend strength, applied machine learning knowledge, startup or core product ownership, and on-site work in New York City.

Supports asphalt pavement research through materials and performance data analysis, quality control, experimental design, sensor coordination, database development, visualization, and research reporting. The role applies advanced analytical or AI-based techniques, helps develop test plans and tools, identifies asphalt research needs, and implements technologies to improve pavement data collection and analysis. This is an on-site position in Gainesville, Florida, requiring collaboration with research and technical teams.

Develops and maintains database systems, analytical tools, data visualizations, reporting processes, and data governance practices for pavement research. Collaborates with research staff to improve data collection, processing, analysis, and presentation. Integrates artificial intelligence into analytical workflows, supports automated reporting, documents software and data workflows, and implements new technologies and infrastructure for continuous improvement.

Faculty investigators will develop independent biomedical research programs in areas such as human genetics, genomics, immunology, aging, cancer, disease modeling, computational biology, data science, and AI-enabled discovery. Responsibilities include conducting innovative research, building collaborations, pursuing translational opportunities, mentoring trainees, contributing to team science, and providing intellectual leadership appropriate to the faculty rank.

Legal Engineers use legal expertise and AI knowledge to lead discovery, demos, pilots, trainings, onboarding, and custom workflow solutions for law firms and corporate legal teams. They build trusted relationships with legal stakeholders, translate legal workflows into Harvey solutions, collaborate with sales, customer success, product, and engineering teams, and provide feedback that shapes the product roadmap. Travel may occur 2–3 or more times monthly for significant business needs.

Own post-activation lifecycle marketing from onboarding through retention, designing personalized, segmented campaigns across email, SMS, in-app messaging, and push. Analyze performance data, run experiments, optimize engagement and lifetime value, and govern the communications ecosystem. Partner with Engineering and Operations on cross-functional initiatives while using AI tools to automate analysis, research, and campaign development.

Lead the design, development, and deployment of autonomous AI agents and multi-agent systems. Build agentic workflows using planning, reasoning, memory, tool calling, RAG, vector databases, and orchestration frameworks. Integrate agents with enterprise systems and APIs while implementing evaluation, monitoring, observability, guardrails, and secure production practices. Provide technical leadership and collaborate across product, engineering, and business teams.

Lead product marketing for Faros in the AI engineering market. Own messaging, positioning, website copy, product launches, campaigns, sales enablement, competitive intelligence, gated assets, customer proof, technical content, and webinars. Partner with company leadership, product, sales, and demand generation teams while maintaining high standards for technical accuracy and writing quality. Track pipeline and campaign impact to refine strategy and influence engineering buyers, analysts, and investors.

Advise federal executive stakeholders, translate concepts into actionable recommendations, manage program performance measures and data processes, and support government contract workstreams. The role requires expertise in federal policies and operations, service delivery, data analytics, KPI development, process improvement, and communicating data-driven findings. Desired experience includes research, surveys, data governance, intelligent automation, AI-enabled capabilities, and DHS support.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Design, deploy, and scale production AI systems and applications. Responsibilities include building data pipelines, tuning and hardening models, collaborating with researchers, engineers, and product managers, monitoring live models, tracking AI advances, conducting code reviews, and owning solutions end to end amid changing priorities.

Research and develop novel AI models, algorithms, and systems; validate them through rigorous experiments and large-scale distributed training; and transition prototypes into production. The role involves deep learning, LLM pre-training and alignment, efficient GPU/TPU utilization, scientific analysis, technical writing, mentoring, code and paper reviews, open-source contributions, and end-to-end project ownership.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

MBA summer associates join Roland Berger consulting teams for 8–12 weeks, working on real client projects across global offices. Interns apply strategic thinking, analytical and creative problem-solving, teamwork, entrepreneurial skills, and AI and data capabilities to complex business challenges. Outstanding performers may receive full-time offers.

Own the strategy, roadmap, and investment priorities for foundational agricultural data products, including pipelines, data models, APIs, and shared services. Partner with engineering, architecture, data science, and product teams to deliver scalable, governed, high-quality data capabilities. Prioritize initiatives based on customer and commercial value, define success metrics, resolve cross-team tradeoffs, and expand data support for analytics, predictive modeling, machine learning, and AI applications.

Defines scalable customer-facing software, platform, and system architectures for Bose Automotive’s Software-Defined Vehicle solutions. Translates customer and market needs into reusable platform capabilities, reference architectures, integration frameworks, deployment models, and proof-of-concepts. Advises OEMs, Tier-1 suppliers, partners, Business Development, Product Management, and Engineering; influences technology roadmaps; supports strategic pursuits; and advances ecosystem integration, platform reuse, and scalable software deployment across vehicle programs.

Build and operate AI agent applications that analyze enterprise code, identify and prioritize security risks, propose and verify fixes, and open merge-ready pull requests. Develop context engineering, evaluation benchmarks, and reliable production systems across thousands of repositories. Partner with customers on integrations, review code, and mentor engineers. Requires deep Python expertise, agentic workflow experience, application security knowledge, and production software development experience.

Lead and scale the engineering organization during rapid growth. Responsibilities include hiring and coaching engineering managers, designing organizational structures across time zones, establishing planning and performance processes, partnering with leadership on workforce strategy, and representing engineering externally. The role requires technical judgment in applied AI and production infrastructure, experience scaling engineering teams, strong communication, and on-site work in New York City.

Lead large cross-product initiatives within ServiceNow’s Security Design team, creating AI-native product experiences, cohesive security workflows, and end-to-end user journeys. Provide strategic design direction across multidisciplinary teams, develop high-fidelity product specifications and design frameworks, influence product strategy, and improve design processes. The role focuses on security lifecycle experiences including SOC automation, threat intelligence, exposure management, vulnerability response, and incident response.

Manage and scale Airwallex’s revenue systems and GTM technology stack. Responsibilities include improving revenue data, integrations, automations, and processes; partnering with Revenue, Marketing, Data, and Product teams; supporting and training users; translating business needs into scalable Salesforce-based solutions; and communicating recommendations to senior commercial leaders.

Build AI-native developer tools, spec-to-implementation workflows, code-generation systems, internal copilots, coding harnesses, sandboxes, authorization layers, and verification guardrails. Develop customer-facing AI functionality within a data-heavy enterprise SaaS platform, collaborate across engineering, product, and operations, and coach teams on safe AI-assisted development practices.

Owns the collection, analysis, publication, and maintenance of ServiceNow implementation insights and product guidance. Develops implementation guides, videos, blogs, and process content; collaborates with product, engineering, design, consultants, customers, partners, and technical writers; researches customer challenges; manages content quality and contractors; supports go-to-market activities; and serves as a thought leader for designated workflows.