Top Artificial Intelligence Jobs

Develop and operationalize generative AI workflows using Claude Cowork, train business users, facilitate workshops, and support AI adoption across business units. Translate business needs into AI-enabled solutions through prompt engineering, workflow design, testing, and validation. Create reusable templates, playbooks, and best practices while monitoring adoption challenges, prioritizing use cases, and reporting progress to stakeholders.

Develop data-driven solutions using Python and R; perform statistical analysis, data wrangling, and machine learning model development with TensorFlow and Scikit-Learn. Collaborate with cross-functional teams to deliver actionable insights, predictive analytics, and automation. The role also requires software engineering experience, familiarity with agentic AI frameworks or coding agents, and an active TS/SCI clearance with polygraph.

Develop data-driven solutions using Python and R; conduct statistical analyses; design and train machine learning models with TensorFlow and Scikit-Learn; wrangle and prepare raw data; and collaborate with cross-functional teams to deliver actionable insights. The role requires extensive software engineering experience, agentic AI or AI coding-agent expertise, active TS/SCI clearance with polygraph, and a relevant bachelor's degree or equivalent experience.

Build production AI retrieval and knowledge systems for rare disease research at NIH. Model biomedical ontologies in PostgreSQL, develop semantic search, retrieval-augmented services, ranking, evaluation, and observability, and deliver user interfaces with Next.js, React, and TypeScript. Deploy and operate services in on-premises and HPC Kubernetes environments, including GPU inference. Collaborate with NIH staff, clinicians, and researchers, and contribute to scientific publications.

Translates business requirements into scalable AI and GenAI solutions, supporting implementation, model deployment, testing, systems integration, customization, scripting, and troubleshooting. Partners with business users, data scientists, and ML engineers; gathers product feedback; trains teams on prompt engineering and AI operations; and assesses risks involving privacy, bias, ethics, and model limitations. Requires autonomous stakeholder advising and mentoring of junior analysts.

Own month-end close, reconciliations, investment accounting, compliance filings, and recurring reporting. Apply AI and automation to streamline accounting and investment workflows while maintaining GAAP, statutory, regulatory, and internal-control requirements. Map processes, implement improvements, document and train users, standardize accounting for new insurance programs, and support a data-driven finance organization.

As a Staff Software Engineer, you will lead technical strategies, develop APIs and microservices, and mentor engineers for innovation at GEICO.

Build and maintain AI/ML capabilities for Blue Ring’s satellite ground systems, including multi-agent telemetry analysis, anomaly detection, conversational retrieval, RAG pipelines, and LLM integrations. Develop and evaluate time-series models, support retraining workflows, and deploy containerized AI services on AWS. Contribute to observability, audit trails, incident response, human-in-the-loop operations, and production support while collaborating with senior engineers.

Leads enterprise data engineering, AI engineering, and data management teams. Owns strategy, roadmaps, and delivery for governed data products, cloud-native platforms, lakehouse architectures, semantic intelligence, and AI-enabled analytics. Drives AWS modernization, DataOps, MLOps, governance, security, reliability, responsible AI, and platform adoption while partnering with business and technology executives. Builds organizational capability, recruits talent, and translates complex data and AI investments into measurable business outcomes.

Lead the design, development, testing, and deployment of enterprise Java applications and scalable microservices. Work directly with clients to translate business needs into technical solutions, build proof-of-concepts and production applications, and deploy Devin AI across engineering teams. The role involves Spring Boot development, distributed and event-driven systems, secure architecture, software modernization, and AI-assisted development workflows.

Design and own end-to-end computer vision and machine learning pipelines for deployed robotic fleets, covering data collection, training, sensor fusion, evaluation, edge deployment, and telemetry. Develop data engines with auto-labeling, synthetic data, and observability loops; integrate perception with navigation and planning through ROS 2. Evaluate emerging AI methods against latency, cost, determinism, and safety constraints. Mentor engineers, collaborate across hardware and software teams, and occasionally advise clients.

Designs, builds, and deploys production-grade LLM applications, including RAG pipelines, enterprise copilots, agentic workflows, and natural-language data interfaces. Develops ML models, Python pipelines, evaluation frameworks, and intelligent automation integrated with Snowflake, Power BI, Fabric, and enterprise systems. Collaborates with data, platform, governance, and business teams to deliver scalable, secure, and responsible AI solutions.

Lead end-to-end intake, evaluation, onboarding, and governance for third-party AI-powered agents in the Agent Marketplace. Build risk-tiered onboarding models, governance frameworks, and lifecycle controls while partnering with product, engineering, legal, compliance, risk, and security. Present recommendations to leadership and continuously improve processes to scale the marketplace securely and compliantly.

Build and optimize infrastructure for Netflix’s machine learning and AI systems, including distributed training, offline and post-training platforms, GPU-optimized inference, and serving. Collaborate with modeling teams on model-system codesign and solve large-scale infrastructure challenges. The internship lasts at least 12 weeks and is intended for PhD students returning to school afterward.

Conduct applied machine learning research at Netflix across areas such as recommender systems, NLP, computer vision, causal ML, agentic AI, multimodal modeling, reliable ML, and model efficiency. Develop and train models using modern frameworks and GPUs, apply distributed training and inference, build end-to-end ML pipelines, and communicate research findings. The 12-week onsite internship is based in Los Gatos, California, beginning in January 2027.

Leads the application of AI across business workflows by identifying high-value use cases, advising teams on approved AI platforms and Microsoft 365 capabilities, and translating technical possibilities into business guidance. Partners with security, governance, legal, risk, and compliance stakeholders to address data access, permissions, and operational risks. Oversees solution evaluation, documentation, governance review, rollout, ownership, feedback collection, reusable patterns, and AI adoption best practices.

Designs and leads prompt strategies, agentic workflows, evaluation frameworks, and LLM application architectures. Ships LLM-powered products to production, develops reusable design patterns and tooling, evaluates nondeterministic systems, applies responsible AI principles, mentors engineers, and influences technical direction. The role also involves RAG, embeddings, vector databases, modern LLM APIs, agent frameworks, multi-agent systems, fine-tuning, and enterprise integrations.

Optimize large-scale neural network training and inference for throughput, latency, and cost. Responsibilities include GPU kernel optimization, distributed system tuning, memory and parallelism optimization, compiler-level performance work, profiling, debugging, model compression, and production measurement. The engineer will collaborate across teams, contribute to technical reviews, mentor junior engineers, and deliver reliable AI systems. Preferred experience includes production-scale LLM inference, major ML systems projects, custom kernels, AI FinOps, and performance publications or talks.

Designs and operationalizes large language model fine-tuning workflows using supervised, preference-based, and reinforcement learning. Responsibilities include dataset construction, evaluation methodology, distributed training, GPU cluster operations, failure recovery, and production deployment. The engineer collaborates with cross-functional teams, contributes to design and code reviews, mentors junior engineers, and ships impactful LLM work. Preferred experience includes multimodal fine-tuning, synthetic data, dataset distillation, open-source contributions, and responsible AI evaluation.

The AI Scientist will conduct applied AI research and build production-grade machine learning systems. Responsibilities include framing problems, training and fine-tuning deep learning models, evaluating research techniques, developing scalable pipelines, deploying models with observability and safeguards, and driving continuous improvement. The role requires collaboration across research and engineering, communication of complex methods, and delivery of impactful AI projects. Preferred experience includes large language models, retrieval-augmented generation, agentic and multimodal systems, responsible AI, and open-source contributions.

Leads Finance Analytics strategy, team development, scalable data products, production-grade data models, dashboards, intelligent workflows, AI applications, and automation. Partners with Finance, Engineering, and senior stakeholders to improve decision-making, data quality, operational efficiency, and adoption of reusable systems. Oversees hiring, mentoring, roadmap execution, data governance, experimentation, and finance-focused technology transformation.

Leads the integrated program for a Physical AI team, coordinating hardware builds, lab readiness, validation, engineering milestones, dependencies, risks, and cross-team decisions. Establishes planning cadences, dashboards, reporting processes, RACI frameworks, and program-management tools. Translates technical status into leadership updates, drives mitigation plans and accountability, facilitates hardware-software tradeoffs, and manages scope changes across engineering, operations, supply chain, facilities, IT, and external partners.

Design, build, deploy, and operationalize LLM applications, RAG pipelines, enterprise copilots, and agentic workflows. Integrate AI with Snowflake, Power BI, Microsoft Fabric, and structured enterprise data. Develop machine learning models, Python pipelines, evaluation frameworks, and intelligent automations. Partner with analytics, engineering, platform, and business teams to deliver production-grade AI solutions, responsible AI practices, natural-language interfaces, automated reporting, data-quality monitoring, and decision-support capabilities.

Owns the build, hardening, patching, configuration, and lifecycle management of enterprise operating-system and platform infrastructure. Leads major incidents, root-cause analysis, change control, service improvement, and technical escalations. Identifies and scales AI and automation for incident triage, runbook execution, predictive monitoring, and reduced MTTR. Provides solution designs, implementation plans, standards, documentation, stakeholder influence, team mentoring, vendor coordination, and AI governance.

Develop and extend agentic LLM-based systems and internal AI tools, design and build APIs and full‑stack features, write and run unit and automation tests, triage production incidents, enforce code standards, and mentor junior engineers to deliver scalable, well-tested financial technology solutions.