Top AI & Machine Learning Jobs

Leads enterprise AI product go-to-market strategy, platform releases, adoption, onboarding, training, documentation, and support. Advises senior leaders, evaluates AI readiness, coordinates cross-functional delivery, manages technical risks and dependencies, and gathers user insights for roadmap prioritization. The role also drives responsible AI governance, executive communications, adoption dashboards, and scalable enablement across cloud and on-premises environments.

Leads enterprise adoption and go-to-market strategy for AI and machine learning platforms. Coordinates product, engineering, and business stakeholders through platform releases, onboarding, training, documentation, governance, and support. Advises senior leaders, evaluates technical readiness, shapes roadmaps, resolves complex implementation challenges, and develops AI opportunity pipelines. The role translates technical capabilities into business outcomes through executive communications, adoption dashboards, risk metrics, and customer-centric enablement across cloud and on-premises environments.

Leads enterprise AI and machine learning platform adoption, go-to-market strategy, releases, onboarding, training, documentation, and support. Partners with product, engineering, business, governance, security, and risk teams to manage dependencies, improve workflows, prioritize roadmaps, and ensure responsible AI readiness. Advises senior leadership, evaluates technical and data readiness, develops AI opportunity pipelines, and communicates adoption, business impact, quality metrics, and risk posture through executive-level roadmaps and dashboards.

Leads a software engineering team building AI-native and cloud-native applications on the ServiceNow platform. Responsibilities include people management, hiring, coaching, technical strategy, architecture, delivery accountability, customer engagement, organizational leadership, and responsible AI adoption. The role drives team performance, product execution, AI/ML integration, platform evolution, engineering quality, incident response, and cross-functional alignment while developing engineers and future leaders.

Leads enterprise AI architecture, implementation, demonstrations, and customer advisory engagements for ServiceNow AI capabilities. Partners with customers, Product Management, Engineering, Sales, and Customer Excellence to pilot and scale emerging AI solutions. Develops implementation playbooks, enablement content, training, thought leadership, and repeatable adoption patterns while influencing product strategy and go-to-market readiness. Applies expertise in generative, agentic, and enterprise AI to deliver measurable business outcomes.

Lead customer-facing design, deployment, and operation of agentic AI solutions on the ServiceNow platform. Analyze customer workflows, architect integrations and guardrails, deploy solutions to production, verify reliability and security, establish reusable technical standards, mentor engineers, advise executives, and feed field learnings back into product and engineering.

Uses advanced analytics, business intelligence, and data science to deliver strategic insights and solutions for AML and sanctions clients in banking and capital markets. Responsibilities include analyzing complex datasets, developing data pipelines and architecture, creating scalable visualization solutions, collaborating with teams, managing stakeholders, building client relationships, and solving complex problems. The role also involves mentoring and managing others while maintaining professional and technical standards.

Develop and maintain AI-powered full-stack applications across frontend, backend, APIs, databases, and cloud environments. Integrate AI services and automation into enterprise workflows, build secure data pipelines and retrieval systems, apply natural language processing, and support deployment, monitoring, and maintenance. Collaborate with business and technical stakeholders to deliver solutions for risk, compliance, audit, and forensics clients while ensuring data quality, security, and regulatory compliance.

Lead development of interactive safety visualizations, dashboards, analytical applications, data marts, and automated reporting for autonomous-driving safety. Build trusted, performant data products with quality monitoring, frequent refreshes, metric definitions, lineage, and statistical interpretation. Partner with safety engineers, data engineers, program teams, and executives to translate complex safety information into clear decision-support tools and continuous improvement insights.

Develop scalable data science and statistical analytics solutions to monitor emerging vehicle safety hazards. Analyze large structured and unstructured datasets, detect anomalies and trends, support safety investigations and root-cause analysis, automate recurring workflows using Python, SQL/Spark, and Databricks, and communicate findings through visualizations and presentations to technical stakeholders and senior leadership.

Develop scalable statistical and data science solutions for monitoring emerging vehicle safety issues. Analyze large, complex datasets using time series, categorical, multivariate, and sampling methods; support anomaly detection, diagnostics, prognostics, and root cause analysis. Automate analytical workflows, improve safety investigations, communicate findings to stakeholders and senior leadership, and mentor less experienced team members.

Architects agentic robotics systems that enable autonomous machines to plan, decompose tasks, select tools, manage memory and state, reason over long horizons, and connect planning with VLA and behavior generation. Designs application and platform architectures, evaluates and deploys technologies, defines technical roadmaps and standards, and partners with cross-functional engineering teams. The role provides strategic technical leadership, influences senior stakeholders, and guides multiple engineering organizations in delivering scalable AI and robotics capabilities.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Lead a team that builds a frictionless enterprise AI intake, enablement, and governance experience. Design self-service standards, streamline routing and adoption, remove blockers, form cross-functional partnerships, mentor staff, and present executive-level findings to accelerate safe AI deployment across the company.