Top Artificial Intelligence Jobs

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Build backend services, data pipelines, APIs, distributed systems, developer tools, and shared platform capabilities for AI products. Develop ML experiment tracking, artifact management, and automated training and evaluation pipelines. Improve reliability, latency, and throughput while collaborating with Product and ML teams. Own scoped projects involving implementation, testing, maintenance, and clean API design for enterprise and government customers.

Own production reliability for a Kubernetes-native control plane provisioning and operating GPU inference fleets. Responsibilities include managing host lifecycle state machines, troubleshooting GPU, CUDA, InfiniBand, RDMA, and model-serving failures, extending automated remediation, owning SLOs and error budgets, leading incident response, improving inference performance and efficiency, and partnering across infrastructure, ML platform, research, and product teams.

Designs enterprise data and AI governance operating models, risk-tiering frameworks, lifecycle stage gates, and practical controls aligned with major AI governance standards and regulations. Uses Microsoft Purview and Fabric to implement governance, privacy, security, and insider-risk controls. Partners with sales, marketing, and practice leaders to shape offerings, lead client engagements, and develop the consulting team.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Leads Crunchyroll’s enterprise-wide AI enablement strategy, roadmap, governance, and adoption. Identifies opportunities to redesign workflows with AI, agents, and automation; translates business needs into scalable solutions; partners with Engineering, Data, IT, and Enterprise Technology; manages external experts and vendors; builds AI learning programs, playbooks, communities of practice, and measurable adoption capabilities; and leads a lean, high-performing team.

Supports Axiom Space’s EVA team by developing, releasing, maintaining, and retiring detailed work instructions and procedures for assembling, integrating, and testing spacesuit and tool hardware. Interprets engineering drawings, technical requirements, and production methods to create accurate manufacturing documentation. Collaborates with engineers, technicians, managers, and subject matter experts while supporting project schedules, quality objectives, and critical milestones.

Supports the aerospace assembly, integration, and testing team by creating, releasing, maintaining, and retiring detailed manufacturing work instructions and procedures. Interprets engineering drawings, specifications, and technical requirements; collaborates with engineers, technicians, managers, and subject matter experts; and helps maintain production and project schedules. The role requires strong technical writing, organization, problem-solving, communication, and aerospace manufacturing experience.

Develops secure, scalable software solutions for JPMorganChase Asset Management. Responsibilities include architecture and application development, production coding, troubleshooting, database optimization, cloud deployment, event processing, infrastructure automation, and responsible use of AI-assisted development tools. The engineer also analyzes data, creates technical artifacts, supports application resiliency and security, and contributes to agile team practices and reusable engineering patterns.

Build and own production-grade AI systems for the People function, including agentic workflows, RAG pipelines, data integrations, evaluation frameworks, and privacy controls. Partner with People, data, governance, and engineering teams to transform operational workflows, ensure reliable use of sensitive workforce data, and promote responsible AI deployment. The role requires independently translating ambiguous business needs into scalable technical solutions and enabling sustainable AI ownership across the organization.

Build and ship end-to-end product features, debug and resolve production issues, partner with Product on UX and direction, use AI heavily for development and prototyping, and iterate quickly across the stack to improve quality and speed.

Lead the continuous improvement of Pursuit’s AI-native learning experience, including curriculum, progression models, learner outcomes, facilitator workflows, and community design. Use market feedback, learning data, and emerging AI tools to refine programs, support personalization, and build AI-powered training products. Collaborate with Product, Programs, Jobs, Development, facilitators, and external partners to adapt learning experiences for diverse audiences and formats.

Leads procurement strategy for AI rack-scale fabric commodities, including market and supplier analysis, supplier selection, negotiations, contracting, performance monitoring, risk mitigation, cost management, and business continuity. Partners with stakeholders to translate priorities into procurement plans and uses financial and supply chain modeling tools. Advises business representatives on contract risks and may lead cross-functional projects.

Supports pricing, packaging, margin analysis, and commercialization of AI features. Responsibilities include modeling API and inference costs, analyzing usage and unit economics, evaluating credit and usage-based pricing, defining SKUs and entitlements, modeling promotions, monitoring commercial performance, researching AI providers and competitors, and presenting findings to cross-functional stakeholders. Partners with Finance, Data Science, Product, Engineering, and Billing teams.

Designs enterprise IT and AI architectures, establishes technical standards, creates architecture roadmaps, and advises business and technology leaders. Develops reusable AI patterns involving generative AI, large language models, retrieval-augmented generation, and AI agents. Partners with cybersecurity, identity, data governance, and development teams to ensure secure, compliant, and responsible AI adoption. Mentors stakeholders and evaluates emerging technologies across cloud and enterprise IT environments.

Develop physics-based robotics simulations, digital twins, software- and hardware-in-the-loop test benches, regression tests, scenario-generation methods, and synthetic data workflows. Build tools that enable engineers to run simulations and parameter sweeps, compare virtual models with real-world data, and evaluate robotics and Physical AI systems. Collaborate across mechanical, electrical, controls, software, and AI engineering teams to improve simulation fidelity and identify failure modes.

Lead strategy, roadmap, governance, adoption, and delivery for Morgan Stanley’s enterprise AI Development Platform. Build and manage a high-performing product organization, oversee AI product portfolios, and partner with engineering, security, risk, governance, and business stakeholders. Drive responsible AI implementation across models, cloud platforms, data, developer tooling, vendors, observability, and regulatory requirements while communicating priorities and outcomes to senior leadership.

Build and maintain AI foundations and production model services, including fine-tuning and deploying open-source models in GPU-backed infrastructure. Develop computer vision, image understanding, OCR, document layout analysis, and multimodal solutions for PDFs, images, and scanned documents. Optimize inference performance, resource utilization, throughput, and latency while debugging production systems and improving engineering standards.

Own end-to-end development of production-scale AI-powered applications that improve internal workflows and customer experiences. Architect agentic systems, RAG applications, tool-using assistants, evaluation and monitoring platforms, shared APIs, guardrails, and infrastructure. Identify high-leverage opportunities, establish technical standards, influence cross-functional direction, mentor engineers, and operate effectively in a greenfield environment.

Leads and grows an engineering team building Harvey’s AI platform, including model routing, agent orchestration, tool and context infrastructure, evaluation, monitoring, and reliable LLM-powered systems. Partners with Product, Research, and engineering teams to establish reusable platform capabilities, set technical direction, optimize quality, latency, cost, and security, evaluate emerging AI technologies, and hire and develop high-performing engineers.

Own strategic enterprise accounts by building C-suite relationships, driving renewals and license expansion, increasing professional services adoption, delivering measurable business value, and coordinating global internal teams. The role carries full commercial responsibility for a portfolio of complex accounts, including multi-year account planning, executive engagement, worldwide deployment coordination, pipeline discipline, and achievement of license, renewal, and services targets.

Architects and deploys secure, scalable AWS cloud, data, and generative AI solutions for enterprise clients. Leads cloud migrations, legacy modernization, Infrastructure as Code, CI/CD, serverless and containerized implementations, and GenAI workloads using Bedrock and SageMaker. Acts as a client-facing technical leader, presents to executives, builds proofs of concept, identifies cloud expansion opportunities, and mentors engineers while developing reusable delivery accelerators and best practices.

Designs and implements secure, scalable AWS cloud architectures, modernizes legacy applications, and deploys generative AI and machine learning solutions using Bedrock, SageMaker, and vector databases. Leads enterprise client engagements, migrations, proofs of concept, and executive technical alignment while identifying cloud adoption opportunities. Provides mentorship, develops reusable infrastructure modules, and promotes AWS Well-Architected delivery practices.

Brings up and optimizes foundation models on SambaNova’s AI platform. Profiles performance across compiler, runtime, and hardware layers; identifies bottlenecks; applies quantization, kernel fusion, scheduling, and memory optimizations; benchmarks inference throughput and latency; and collaborates with machine learning, compiler, runtime, and hardware teams to deliver product improvements.

Designs, develops, deploys, and supports enterprise AI solutions across Northern Trust’s Digital Coworkers and Generative AI Foundation Platform. Responsibilities include full-stack application and API development, Azure cloud engineering, Terraform infrastructure, CI/CD, LLM and RAG integrations, AI agents, workflow automation, monitoring, governance, and production support. The role partners with engineering, architecture, security, product, and business teams to deliver scalable, secure, governed AI capabilities in a regulated financial-services environment.