Top Remote AI Engineer Jobs

Lead AI technical strategy, architect AI systems, set safety standards, mentor engineers, and evaluate AI models at Midi Health, a healthcare-focused company.

You will design and build LLM-powered features, integrate AI capabilities into workflows, and contribute to internal practices at a healthcare-focused company.

The Senior AI Engineer will develop and deploy multi-agent systems, automate workflows, integrate enterprise systems, and mentor junior engineers while ensuring quality and security compliance.

This role leads data and AI engineering strategies for R&D, oversees a team of engineers, and ensures compliance with regulatory standards while fostering innovation and collaboration across functions.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

The Staff AI Engineer will build the AI platform, apply LLMs for intelligent feature development, and ensure system scalability and security while optimizing performance.

The Senior AI Engineer will design and implement scalable AI platform services, integrate large language models, and optimize AI capabilities within ClickUp's product, collaborating across teams for innovation.

Build and lead a greenfield cloud platform supporting application, data, and AI workloads. Responsibilities include managing Terraform infrastructure, production EKS clusters, Helm deployments, CI/CD pipelines, security controls, observability, cloud cost allocation, and developer golden paths. The role establishes engineering standards, supports regulated-data compliance, partners across engineering teams, reviews infrastructure changes, mentors engineers, and writes production code while ensuring reliable, secure, and cost-effective service delivery.

Build and operate custom AI agents for private-market clients end to end, from discovery and requirements through deployment, rollout, adoption, and ongoing iteration. Design agentic systems with retrieval, context design, tool and MCP integrations, orchestration, evaluation, observability, and cost controls. Work directly with clients, make product and engineering trade-offs, manage cloud infrastructure, document solutions, train users, and mentor less experienced engineers.

Build and operate custom AI agents for private-market clients across discovery, requirements, design, full-stack development, deployment, and post-launch iteration. Translate ambiguous business needs into reliable software, integrate tools and client systems, establish evaluation and observability practices, and support adoption through documentation, training, and rollout. Collaborate directly with clients and internal platform teams while balancing speed, security, maintainability, and product impact.

Build and improve Swayable’s AI and analytics engine using Python, scientific computing, machine learning, and distributed systems. Develop scalable, high-performance software, optimize parallel computing architecture, productize AI and data analysis capabilities, and collaborate with engineers, data scientists, and researchers. The role also includes designing new features, maintaining testable code, mentoring junior engineers, documenting decisions, and providing technical feedback.

Build and optimize Swayable’s AI and analytics engine using Python, scientific computing, machine learning, and distributed systems. Responsibilities include architecting scalable high-performance computing solutions, productizing AI and data analysis, developing new features, maintaining code quality, collaborating with engineers and researchers, and mentoring junior engineers.

Designs, develops, deploys, and maintains scalable full-stack AI applications, APIs, integrations, backend systems, databases, and cloud deployments. Collaborates with AI/ML scientists and healthcare domain experts, makes architectural decisions, writes and reviews code, mentors engineers, integrates external data services, and optimizes application performance and security. The role is fully remote across the country with up to 5% travel and no on-call requirements.

The AI Deployment Engineer will build, deploy, and scale AI solutions while collaborating across departments to ensure successful implementation and support of operational workflows.

Own the continuous improvement of customer-facing AI products by building evaluation frameworks, repeatable test sets, experiments, analytics, and quality-monitoring processes. Improve usefulness, consistency, reliability, latency, and cost while diagnosing failures across prompts, context, models, UX, and data. Partner with Product, Growth, Design, Engineering, QA, and Analytics to connect AI behavior to activation, conversion, retention, and satisfaction metrics in a remote, async environment.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.

Design, build, deploy, and operate production-grade AI agents and multi-agent systems. Implement RAG, agent orchestration, copilots, observability, memory patterns, and enterprise integrations while ensuring governance, reliability, and onshore/offshore delivery.

Design, build, and deploy production-grade AI applications, agents, retrieval systems, integrations, and workflows for enterprise customers. Lead technical discovery, architecture workshops, prototyping, troubleshooting, deployments, and go-live support. Translate customer needs into scalable solutions and reusable engineering patterns, while providing product feedback that informs roadmaps. Partner with Product, Sales Engineering, Customer Success, and Engineering teams, and mentor customer-facing engineers. Travel to customer sites as needed.

Build and own full-stack AI product features from concept through launch, adoption, and iteration. Translate user, partner, product, and enterprise requirements into reliable experiences for AI chat, search, research, workflow automation, administration, and integrations. Collaborate across engineering, product, design, enterprise teams, and open-source contributors. Evaluate AI tooling, make technical and product tradeoffs, write maintainable tested code, measure product adoption and reliability, and establish reusable patterns for an enterprise-ready open-source platform.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Lead design and delivery of AI-driven product solutions across the commerce platform. Own high- and low-level architecture, implement scalable RAG/LLM systems, select AI infrastructure, mentor teams, and collaborate with product and engineering leadership to execute AI strategy and rapid experimentation.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Lead design, development, deployment, and scaling of cloud infrastructure and SRE tooling. Build automation, CI/CD, observability, capacity planning, and reliability improvements; collaborate with product teams to define non-functional requirements and resolve production issues.