Top Remote AI Engineer Jobs

Leads enterprise adoption of AI-native engineering tools, workflows, and practices. Researches and prototypes emerging platforms, develops proofs of concept and reference architectures, and partners with engineering teams to operationalize solutions. Owns AI training and enablement through documentation, presentations, tutorials, videos, office hours, and executive briefings. Evaluates tooling, optimizes development processes, supports organizational change, and gathers feedback to improve engineering standards and developer experience.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

Conduct research and engineering across computer vision, vision-language models, 3D perception, and physical AI. Build and fine-tune perception and multimodal models, develop perception-to-action policies, curate datasets, design evaluations, run rigorous experiments, and deploy reproducible services using modern AI infrastructure. The role translates published research into prototypes and production-ready modules for robotics, simulation, video understanding, and embodied intelligence.

Design, secure, and scale GCP cloud infrastructure supporting event-driven services, AI models, and automated releases. Maintain infrastructure as code and CI/CD automation, manage access controls and encryption, remediate penetration-test and vulnerability findings, and own monitoring, dashboards, on-call response, incident management, and performance improvements.

Build and enhance user-facing AI applications, frontend interfaces, backend services, APIs, data pipelines, semantic models, dashboards, and analytics solutions. Integrate AI tools with enterprise systems, support deployment and monitoring, validate pipeline and AI output quality, implement reporting security controls, and provide Level 3 product support. Collaborate with technical, clinical, operational, and business stakeholders while following software engineering, testing, security, privacy, and governance standards.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

Build and maintain scalable full-stack web applications across front-end and back-end systems. Develop RESTful APIs, integrate AI and machine-learning models using Python, manage SQL and NoSQL databases, and create GitHub Actions CI/CD pipelines. Collaborate with data scientists, troubleshoot and improve existing software, participate in code reviews, and apply modern DevOps, cloud, containerization, and microservices practices.

Leads software engineering efforts for production applications, including development, testing, deployment, monitoring, security, resilience, and performance tuning. Designs reusable code and architecture documentation, collaborates with product teams, supports agile delivery, and mentors junior engineers. Contributes to technical roadmaps, cross-functional initiatives, platform adoption, system modernization, and AI-driven engineering productivity improvements.

Builds developer and customer relationships across the Jetson ecosystem, guides partners through embedded Linux and Jetson software integration, promotes NVIDIA AI libraries and edge AI capabilities, supports production deployments, and communicates developer feedback to engineering and product teams to shape platform roadmaps, reference designs, positioning, and adoption.

Lead the development of AI products, design scalable systems, advance AI foundations, collaborate with stakeholders, ensure quality practices, and mentor engineers.

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

Lead the AI Platform team in building reliable, secure, observable infrastructure, shared services, deployment patterns, and operational practices for AI capabilities. Set technical priorities, coordinate cross-team dependencies, guide architecture and production operations, improve developer experience, and coach engineers while contributing to complex technical work.

Lead Root’s AI Enablement Squad by defining AI-native ways of working, guiding cross-team adoption, connecting user workflows to AI Platform strategy, and turning experiments into reusable products and capabilities. Set technical direction, lead major initiatives, align senior stakeholders, establish adoption measures, promote responsible AI use, and mentor engineers and technical leaders across the organization.

Build and operate AI infrastructure systems, including telemetry pipelines, observability dashboards, incident-management automation, hardware and software catalogs, service ownership data models, and self-service discovery tools. Lead cross-functional infrastructure initiatives and develop AI-assisted operational tooling. The role requires production software and infrastructure expertise, programming experience, and the ability to improve reliability, visibility, incident response, and operational efficiency across NVIDIA GPU cloud environments.

Lead the AI Enablement Squad by prioritizing embedded AI adoption engagements, translating workflow discoveries into reusable AI Platform improvements, coordinating cross-functional partners, and coaching engineers. Establish repeatable adoption playbooks, protect capacity for scalable capabilities, communicate outcomes and blockers to stakeholders, and guide responsible deployment of AI solutions across the company.

The NLP Scientist will solve complex medical document understanding problems using NLP and CV methodologies, optimize models for performance, and make data-driven decisions.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

The GCP AI Engineer will lead development of AI solutions on Google Cloud to enhance patient care, automate workflows, and ensure operational reliability through integrated AI systems in healthcare data environments.

Act as a part-time external technical mentor for Claude/agentic-AI programs: run 1:1 calls, technical deep-dive presentations, and group Q&A; review student projects, provide guidance on Claude ecosystem, agent design, prompts, production AI systems, and best-practice engineering.

Build and productionize agentic AI workflows and automation for logistics. Integrate LLMs, orchestration frameworks, and data pipelines with TMS/RPA systems. Implement guardrails, monitoring, evaluation metrics, audit trails, and lightweight front-end prototypes. Collaborate with product, ops, and engineering to scale successful prototypes into reliable production services.

The AI Engineer will build AI-powered solutions to enhance productivity and automation across departments, collaborating closely with leadership and managing integrations with existing SaaS platforms.

Build and ship AI-enabled coaching product features from 0-to-1; design application capabilities on top of foundational models; translate coaching workflows into AI agents; build data pipelines and integrations; and educate the team on data and AI best practices.

Build and improve internal AI enablement systems, including reusable journeys, skills, templates, paved paths, integrations, APIs, and development infrastructure. Partner with employees to identify blockers, provide technical guidance, troubleshoot workflows, and translate recurring needs into roadmap improvements. Support AI-assisted development environments, create documentation, and lead office hours, workshops, and enablement sessions. Collaborate with the Smart AI Program lead to execute roadmap priorities and improve internal developer experiences.

Operate and scale production LLM and AI agent platforms across the enterprise. Build evaluation frameworks, observability, deployment safeguards, cost optimization, and governance capabilities. Partner with business and technology leaders to identify high-value AI opportunities, design future-state workflows, define requirements and success metrics, guide implementation, and drive adoption. Ensure AI solutions are reliable, measurable, compliant, and integrated into everyday operations.

Build and maintain scalable Python-based AI applications under senior guidance. Assist with requirements, feature implementation, data modeling using Pydantic, code reviews, troubleshooting, testing, and CI/CD. The role requires collaboration, communication, and willingness to learn modern Python tools, frameworks, and libraries. Candidates should understand object-oriented programming, REST APIs, large language models, and AI coding tools. The position is fully remote within the United States.