Top Artificial Intelligence Jobs

Analyze and optimize AI, compute, storage, and software infrastructure costs through planning, forecasting, budgeting, reporting, variance analysis, and investment analysis. Build financial models, identify efficiency opportunities, translate technical metrics into actionable recommendations, and prepare executive and Board presentations. The role partners closely with engineering, compute operations, FP&A, and strategic finance teams while independently managing ambiguous, high-impact projects.

Leads Coveo’s B2B customer marketing strategy across adoption, engagement, renewal, expansion, and advocacy. Builds lifecycle programs, customer communications, executive engagement, voice-of-customer initiatives, adoption and health programs, and customer content. Coordinates Product Marketing, Campaigns, Field Marketing, Account Management, Customer Success, and other customer-facing teams while developing the customer marketing function and coaching stakeholders.

The Executive Director will lead the strategic growth, partnerships, organizational development, funding, and sustainability of an NSF-funded programmable cloud laboratory. Responsibilities include coordinating scientific and operational priorities, managing institutional and external relationships, integrating shared research infrastructure, overseeing budgets and grants, mentoring senior staff, and monitoring research, education, workforce development, innovation, and outreach milestones.

Senior Solutions Engineer responsible for supporting enterprise sales of identity governance and security solutions. Duties include technical discovery, presentations, RFP responses, proof-of-concept delivery, customer consultation, opportunity management, product feedback, and collaboration with sales, product, marketing, and bid teams. The role also mentors Solutions Engineers and requires expertise in identity security, cloud platforms, DevOps, AI technologies, and technical communication. Boston Metro preferred; remote work with up to 40% domestic travel.

Leads data science work involving data exploration, cleaning, analysis, visualization, mining, predictive modeling, and algorithm development. Analyzes structured and unstructured data using statistical and programming tools, applies machine learning and AI techniques, and translates complex datasets into actionable insights for clients. Guides teammates, develops data-driven systems, and advises decision-makers across public and private sector challenges.

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.

Leads strategy, development, and launch of commercial auto insurance products for emerging mobility models, including rideshare, delivery, fleets, and autonomous vehicles. Owns market vision, identifies coverage and risk opportunities, builds business cases, designs pricing and underwriting concepts, navigates multistate regulation, and develops partnerships with mobility platforms, OEMs, telematics providers, and technology vendors. Drives cross-functional alignment, product performance, AI integration, and innovation in ambiguous markets.

Own and develop SandboxAQ’s intellectual property function, including patent portfolio strategy, invention harvesting, prosecution, lifecycle management, outside counsel oversight, freedom-to-operate analyses, trademark clearance, and trade secret programs. Partner with scientists, engineers, and business teams to protect innovations, inform product strategy, support transactions, and build scalable IP processes in a fast-growing AI-focused company.

Lead financial modeling, valuation, diligence, and transaction execution for acquisitions, venture investments, and strategic partnerships. Build DCF, comparable company, pro forma, and synergy analyses; coordinate cross-functional diligence; evaluate markets and targets; support deal structuring, negotiations, and executive decision-making. Maintain deal pipelines and infrastructure, prepare investment memos and transaction materials, guide associate-level support, and help develop AI-enabled deal processes and market intelligence capabilities.

Provides strategic and technical guidance on emerging AI capabilities for federal clients. Advises leadership on infrastructure, tools, workforce needs, and AI maturity; translates technical concepts into recommendations; gathers mission requirements; develops AI/ML and NLP solutions; creates dashboards, reports, and visualizations; interprets model outputs; and collaborates with stakeholders to support AI-driven decision-making.

Lead DHS and DOJ account growth from opportunity identification through contract award, deployment, renewal, and expansion. Own a multimillion-dollar quota, develop proposals and white papers, structure federal deals, secure executive sponsorship and funding, and transition AI prototypes into enterprise capabilities. Co-design agentic AI solutions with technical teams, manage customer relationships, and negotiate teaming and subcontracting agreements with systems integrators and federal primes.

Participate in an eight-week paid sales academy focused on prospecting, qualifying leads, handling objections, closing, and client communication. Use Salesforce, Gong, ZoomInfo, LinkedIn Sales Navigator, and AI-powered prospecting tools while receiving coaching and performance feedback. Successful participants may advance to a full-time Business Development Representative role, partnering with Account Managers to identify target accounts and generate sales opportunities.

Conduct research on evaluating frontier large language models and AI agents. Analyze model behavior, diagnose capability, reasoning, robustness, and alignment failures, and develop benchmarks for text and multimodal systems. Apply post-training techniques such as supervised fine-tuning, RLHF, and reward modeling to connect failures with training interventions. Collaborate with AI labs, define evaluation best practices, translate findings into technical strategy, and publish research at major conferences.

Supports FP&A and enterprise analytics by developing scalable Power BI reporting, financial models, dashboards, and automated processes. Analyzes financial and operational datasets to identify trends, risks, and opportunities; improves forecasting, budgeting, and recurring reporting workflows using Excel, Python, SQL, Power Automate, and Microsoft platforms. Collaborates with finance, data engineering, IT, and business stakeholders, trains users, maintains governance standards, and contributes to the Pigment planning platform. Evaluates AI-enabled tools to improve reporting efficiency, data quality, and decision-making.

Leads complex technology sourcing initiatives covering software, SaaS, cloud, and emerging technologies. Develops sourcing and supplier strategies, evaluates markets and vendors, manages RFI/RFP processes, negotiates technology agreements, builds business cases, assesses risk and total cost of ownership, and advises cross-functional stakeholders on technology investments, renewals, supplier performance, and commercial decisions.

Own a portfolio of commercial SaaS customers, driving expansion, adoption, net revenue retention, and renewals. The role involves multi-threading with technical and product leaders, managing usage-based commitments, leading renewal negotiations, cross-selling platform capabilities, and developing account tiering strategies. The Account Executive will translate rapidly changing AI products, models, and pricing into customer value narratives while partnering with Product, Support, RevOps, and Sales Leadership.

Lead and grow an Applied AI engineering squad to design, build, and ship GenAI-powered features across Copia's products. Define strategy and roadmap, oversee architecture (frontend, backend, APIs, LLM integration, RAG), ensure reliability and operational quality, mentor engineers, manage hiring and performance, and promote responsible AI practices in a safety-critical domain.

As a Senior AI Engineer, you'll develop ML and Generative AI models, ensure integration with enterprise applications, and manage large datasets while collaborating with teams and mentoring others.

The Director of AI Customer Success will advise enterprise clients, ensuring successful delivery and adoption of AI solutions while driving retention and value realization. Responsibilities include customer strategy, developing success metrics, managing relationships, and collaborating with internal teams.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

As the AI Transformation Lead, you'll drive AI adoption across teams, build AI-powered workflows, and establish foundational systems and metrics for continuous improvement in operations.

Define and deliver reference architectures and design guides for HPE CloudOps Suite private cloud. Drive product roadmap, translate customer and operational needs into actionable designs, specify non-functional requirements, and collaborate with engineering, architecture, sales and partners to ensure scalable, secure, and adoptable solutions.

Develop and maintain Python-based analytics, automated test orchestration, and scenario-generation pipelines for autonomy simulation. Build adapters in C++/Python, analyze logs/rosbags, deploy distributed orchestration with Terraform/Kubernetes, and create telemetry ingestion and reporting frameworks to validate AI Pilot behaviors across secure and commercial environments.

Lead design and deliver cloud-native microservices and AI/LLM-driven features. Architect scalable, secure systems, drive AI-assisted engineering practices, improve observability/automation, mentor teams, and integrate generative AI into business workflows.

Design, train, and deploy deep-learning models that predict market prices; build tooling and large-scale ML systems to enable firm-wide ML adoption; simulate trading decisions and integrate model predictions into production trading systems.