Top Artificial Intelligence Jobs

Lead commercial AI and digital strategy for the Interventional Oncology & Embolization division. Define AI roadmap, governance, KPIs, and digital product strategy; build commercial intelligence, analytics, and predictive capabilities; drive pilots, adoption, change management, and sales productivity improvements; manage and develop a high-performing team and partner across Sales, Marketing, Finance, IT, and Enterprise AI.

Lead full-cycle technical recruiting for engineering and AI teams, owning sourcing, interview design, stakeholder advising, and offer management. Build proactive pipelines, advise on market compensation and hiring strategy, champion recruiting operations and AI-enabled sourcing tools, and continuously improve hiring metrics and candidate experience for senior and principal-level technical hires.

Serve as a strategic talent partner advising executives on hiring strategy, organizational design, and workforce planning. Lead full-cycle recruiting for engineering, AI/ML, infrastructure, and leadership roles while improving recruiting operations, sourcing automation, and candidate experience. Coach managers across the employee lifecycle and use talent intelligence to influence business decisions.

Senior strategic finance partner for AI and Analytics products. Own forecasting, pricing, monetization, investment business cases, KPI/reporting, and lead a small analytics team to guide product, engineering, and GTM decisions.

Lead design, bring-up, validation, and optimization of AI accelerator systems and GPU clusters for production ML workloads. Debug system-level issues across hardware, firmware, and software, build automated test and monitoring tooling, collaborate with vendors and cross-functional teams, and mentor junior engineers.

Lead deployment, optimization, and resource scheduling of vehicle and cloud AI models. Design low-latency inference pipelines, build system stability and profiling frameworks, optimize GPU execution, and scale LLM serving for simulation and validation. Collaborate with perception, cloud, and safety teams to enable reliable, production-grade autonomous deployment.

As a Senior Full Stack Developer, you will build an AI voice agent SaaS platform, work across the stack, lead through code quality, and automate development processes.

Design, build, and operate production AI agent systems using modern LLM APIs and agent SDKs. Own end-to-end architectures, integrations, observability, progressive delivery, and reliability. Mentor engineers, participate in technical design reviews, and collaborate with product peers to deliver scalable, stable legal AI agent tooling.

Own DevOps, maintenance, and support for an in-house Open Web UI serving ~2,500 users. Manage lifecycles across Dev/Prod, triage infrastructure and user issues, maintain CI/CD pipelines, and contribute to AI Platform development within an Agile team.

Embed with Marketplace teams to discover and prioritize AI opportunities, rapidly prototype and validate AI/LLM solutions using enterprise data, and partner with Technology and Data teams to productionize, measure, and drive adoption of scalable automation and ML solutions.

Design, build, and deploy AI-powered features for a SaaS platform by integrating LLMs and cloud AI services. Develop scalable backend services, REST APIs, and data pipelines, prototype AI capabilities, optimize performance, and collaborate across Product, UX, and Engineering to deliver production-ready, well-tested customer-facing solutions.

Lead discovery, prototyping, and delivery of AI/GenAI products. Define PRDs, build prototypes, scope requirements, run evaluations, partner with engineering/data/design, and shepherd solutions through enterprise AI governance to production.

Lead development of AI products from discovery through production: define strategy and roadmap, prototype with agentic tools, translate business problems into scoped AI use cases, own requirements and delivery, design evaluation and quality metrics, support solution architecture, lead cross-functional teams, navigate enterprise AI governance, and mentor product managers.

Lead AI Engineer defines AI strategy, architecture, and standards; makes build vs. buy decisions; architects scalable, reliable LLM/GenAI systems; establishes LLMOps, evaluation, guardrails, and observability; coordinates cross-functional alignment with product and engineering; and champions AI-assisted developer tooling and responsible AI practices.

Lead architecture, development, and operational health of enterprise applications and AI platforms. Mentor junior engineers, set coding and security standards, evaluate new technologies, perform code reviews, and drive platform reliability, performance, and documentation across the organization.

Embed with banks and insurers to discover workflows, design AI-powered solutions, scope pilots, manage 0→1 deployments, drive UAT and compliance approvals, measure ROI, and feed field insights into the product roadmap.

As a senior software engineer at NVIDIA, you will design and build tools for performance analysis of AI workloads, lead automation efforts, and enhance workflows for engineers.

Lead the Revenue Operations AI strategy to identify, prioritize, pilot, and scale AI use cases that boost seller productivity, improve forecast accuracy, and deliver measurable commercial value. Partner cross-functionally to build business cases, run experiments, create measurement frameworks, and drive adoption across sales.

Lead strategy, design, and delivery of enterprise AI/GenAI solutions. Define roadmaps, architect scalable GenAI systems (RAG, vector search, agents), manage cross-functional teams, ensure governance and responsible AI, and drive adoption and KPIs to demonstrate business value.

Lead integration of AI-enabled capabilities into intelligence analysis workflows. Translate mission requirements into ML/NLP use cases, evaluate and validate AI outputs, develop SOPs and best practices, collaborate with analysts, data scientists, and engineers, and apply automation and AI techniques to large structured and unstructured datasets to improve analytic production and decision-making.

Lead strategy, development, and delivery of AI products for Sales & Services. Define product vision and roadmap, manage cross-functional teams, prioritize features, create product artifacts (PRDs, user stories), and measure KPIs. Drive user adoption, run experiments, and collaborate with stakeholders to integrate scalable, secure AI/ML solutions into business applications.

Postdoctoral researcher to develop and evaluate methods for interpreting deep models, including sparse decompositions, concept discovery, mechanistic analysis, and human-in-the-loop workflows. Design evaluation methodologies, conduct independent and collaborative ML research, implement advanced ML methods (e.g., using PyTorch or JAX), document results in publications, and contribute to proposals within scientific and national-security applications.

Lead research and development of advanced AI solutions (LLMs, generative and agentic systems). Prototype, evaluate, and productionize models; set architecture and governance standards; mentor teams; publish research and drive enterprise adoption within a regulated financial services environment.

Lead Americas Hyperscale & AI sales strategy and planning; build and mentor a strategy team; develop annual and long-range GTM plans; drive transformation initiatives; provide data-driven competitive insights using AI/BI and statistical tools; advise executive leadership and align cross-functional teams to optimize resource allocation and sales organization design.

Partner with scientists and product teams to design, prototype, deploy, and scale production-grade cheminformatics and AI-enabled software for drug discovery. Bridge chemistry, data, and engineering to build APIs, data pipelines, containerized services, and platform infrastructure while driving engineering best practices, performance tuning, and user-validated solutions.