Top Artificial Intelligence Jobs

Build, deploy, and operate production AI applications and scalable machine learning solutions. Develop automated training and deployment workflows, support production AI infrastructure, and collaborate with software, data science, and platform engineering teams. The role involves Python development, ML frameworks, containerization, Kubernetes, Linux, and Git, with preferred experience in LLMs, RAG pipelines, model serving, vector databases, frontend development, and ML orchestration.

Design, build, deploy, and operate production AI agents for Cisco Commerce Operations. Responsibilities include agent orchestration, RAG pipelines, enterprise API integrations, guardrails, evaluation, observability, CI/CD, monitoring, cost tracking, and continuous optimization. The role partners with engineering and business teams to automate operational workflows and measure business impact.

Design, implement, and evaluate machine learning and deep learning algorithms for autonomy, trajectory prediction, and perception. Develop predictive and decision-making models, conduct experiments in simulated or dynamic environments, and build scalable research tools. Collaborate on research prototypes involving agent behavior, environmental understanding, and generative AI. Communicate findings to leadership and help transition validated research systems into production.

Lead executive discovery, enterprise AI deployments, and production rollouts across cloud, on-premises, and firewalled environments. Build MCP-powered context layers, resolve SSO and networking challenges, expand customer use cases, shape go-to-market messaging, and feed customer insights into product development. The role requires hands-on LLM and agent experience, strong commercial judgment, technical breadth, and frequent customer-site travel.

As a Director, Data Scientist - Generative AI Systems, you will lead a team to develop AI products using NLP and generative AI technologies, partnering with cross-functional teams. Your role involves leveraging open-source programming and machine learning to convert customer data into actionable insights and enhance customer interactions with financial services.

Design, train, and evaluate Responsible AI components: fine-tune LLMs (including RLHF), build adversarial agent evaluations and safety mitigations, collaborate with engineering to deploy mitigations at scale, monitor operational performance, and troubleshoot end-to-end issues.

Build and operate AI-native, full-stack products for sales, marketing, and revenue operations teams. Develop agent workflows, automations, integrations, and production applications for research, enrichment, account scoring, and other go-to-market needs. Collaborate with nontechnical users to understand workflows, test solutions, and improve products. Integrate systems such as Salesforce, Marketo, and Gong while making pragmatic build-versus-buy decisions.

Lead assembly, integration, and testing of satellite payloads, spacecraft buses, and subsystems. Develop test plans, procedures, and automation scripts; execute functional and environmental tests; troubleshoot integration issues; document results; and coordinate with engineering and program teams to ensure spacecraft meet requirements and launch readiness milestones.

Conduct AI research for surgical robotics using multimodal surgical data, Visual-Language-Action models, computer vision, language models, and robot learning. Develop algorithms for surgical scene understanding, procedure recognition, and imitation learning; process large datasets; validate models in simulation and on robotic systems; collaborate with engineers and clinicians; and document research for technical reports, patents, or conferences.

Designs the strategy and operating framework for AI-driven demand generation and outbound prospecting. Translates business needs into functional requirements, system logic, governance guardrails, and decision trees for technical teams. Partners with Sales, SDR, Marketing, Product, and analytics leaders to align initiatives with pipeline and revenue goals. Monitors GTM AI trends, evaluates prospecting effectiveness, and recommends improvements based on conversion and performance metrics.

Build and operate production AI agents, companions, and workflow automations across sales, finance, marketing, and property operations. Integrate Azure, Snowflake, SaaS platforms, and vendor APIs while managing evaluations, monitoring, security, human review, cost, and reliability. Partner with business users and executives to identify high-value opportunities, measure adoption and outcomes, train users, and maintain governance. The role requires strong backend engineering, applied LLM experience, integration expertise, and sound data-security judgment.

Design, implement, and evaluate novel machine learning and deep learning algorithms, develop scalable research tools, benchmark emerging techniques, and transition research prototypes into production AI systems. The role requires collaboration with research engineers, experimentation across data, architectures, training, evaluation, and deployment, and communication of findings to leadership. Focus areas include transformers, multimodal foundation models, diffusion policies, fine-tuning, distillation, mixture-of-experts, and potentially reinforcement, self-supervised, imitation learning, and robotic VLA models.

Develop, validate, and deploy ML/AI-driven cheminformatics models and workflows to predict molecular properties and inform hit-to-lead and lead optimization. Collaborate with medicinal chemists, biologists, and data scientists to integrate computational solutions, pilot new methods, and deliver actionable compound prioritization and ADME predictions with measurable impact.

Develop, deploy, and maintain ML models and cheminformatics workflows to predict molecular and ADME properties. Process and analyze chemical/biological datasets using cheminformatics toolkits, build SAR and QSAR models, optimize data pipelines, and collaborate with cross-functional teams to integrate AI-driven approaches into drug discovery. Ensure data integrity, reproducibility, and model monitoring.

Own the vision, roadmap, and customer experience for a GPU cloud infrastructure platform. Define provisioning, capacity, scheduling, networking, storage, workload execution, APIs, pricing, and consumption models. Partner with engineering, operations, sales, finance, customers, and executives to improve reliability, utilization, performance, and adoption. Use product and infrastructure data to guide tradeoffs and lead products from discovery through launch and iteration.

Evaluate conversations between users and an AI shopping assistant, identify logical errors, inaccuracies, and poor recommendations, and create structured rubrics and verifiers for judging response quality. This remote engagement requires at least 20 hours per week and focuses on improving model performance and e-commerce response quality.

Build enterprise AI-agent applications using React and TypeScript, owning features from frontend architecture through backend integration, testing, deployment, and production support. Develop interfaces for agent progress, approvals, human review, results, errors, and multi-step workflows. Integrate agent orchestration, APIs, tools, retrieval, and enterprise systems while contributing to backend services. Participate in architecture and code reviews and apply strong reliability, testing, debugging, and performance practices.

Serves as Danaher’s lead legal advisor on artificial intelligence, advising on AI regulations, privacy, healthcare, technology, intellectual property, contracting, data governance, and compliance. Partners with technical and business teams to embed legal requirements into AI development and deployment. Supports AI risk assessments, executive approvals, vendor agreements, privacy impact assessments, cross-border data transfers, and responsible AI governance.

Leads HubSpot CRM implementation, governance, optimization, integrations, data migration, and user adoption. Develops business intelligence strategies, dashboards, reports, and actionable insights for sales, marketing, and executives. Drives sales enablement through training, documentation, change management, and performance reporting. Partners with marketing on campaigns, segmentation, event tracking, and ROI measurement while identifying automation, AI, and process-improvement opportunities.

Lead the architecture and delivery of scalable, secure enterprise AI and generative AI solutions. Design systems using LLMs, RAG, AI agents, embeddings, vector databases, and cloud AI platforms. Establish architecture standards, governance, security, and responsible AI practices; evaluate models and platforms; integrate solutions with enterprise applications; lead technical reviews and proof-of-concepts; and guide implementations from concept through production. Mentor engineering teams and communicate AI strategies to technical and business stakeholders.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Build and operate bare-metal AI and Kubernetes platforms, deploy and optimize large language models, and transition provisioning to Terraform- and Ansible-managed infrastructure. Responsibilities include OpenShift administration, platform observability, DNS and core services, cybersecurity compliance, feasibility assessments, documentation, and OCONUS hardware bootstrap support. The role requires strong Linux administration, AI model deployment, infrastructure-as-code, container platform, and DoD environment experience.

Leads cyber and AI systems architecture for complex federal pursuits supporting the Department of War and Intelligence Community. Develops agentic-AI-enabled cyber solutions, evolves offerings, partners with technology providers and research teams, and designs solutions for SOC modernization, threat hunting, and automated cyber defense. Manages and mentors solution architects, establishes scalable solutioning standards, and translates complex technical and mission concepts into compelling proposals and delivery plans.

Develop and maintain AI-powered applications, agents, automations, and integrations for legal and operational workflows. Build LLM tools, RAG systems, APIs, and workflow automations using Python and/or JavaScript/TypeScript. Manage projects from prototype through deployment, evaluate AI performance, maintain secure integrations, and collaborate with attorneys, leadership, and technical teams to improve internal processes.

Support AI and data transformation initiatives for investment research. Collaborate with portfolio managers, research analysts, and data teams to identify use cases, define requirements, prototype AI-native tools, build data pipelines and evaluation harnesses, iterate on agents and prompts, analyze investment data, and translate investment workflows into product specifications.