Top Remote AI Engineer Jobs

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Lead online brand reputation by owning page-one search results, producing SEO-optimized content, building PR relationships, launching customer advocacy, and executing rapid-response campaigns during reputation attacks.

As an AI Native Forward Deployed Engineer, you'll bridge customer operations and product development, implementing technical solutions and enhancing the platform based on field insights.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

As a Full Stack Engineer III at Calendly, you'll develop features, scale the platform, and mentor team members while using various technologies.

As a Senior AI Platform Engineer, you will architect AI-driven systems, develop CI/CD pipelines, and optimize infrastructure for AI workflows.

The Principal Engineer will architect and develop scalable AI-driven data platforms, mentoring engineers and aligning technical strategies with business goals.

Lead the design and development of machine learning solutions, overseeing the entire software development lifecycle and mentoring engineers. Drive innovation in Generative AI and high-performance systems.

Operate and support production AI infrastructure across global datacenters, focusing on high-performance NVIDIA GPU platforms, Kubernetes, and high-speed networking. Monitor and troubleshoot infrastructure, network, hardware, and platform incidents; participate in incident response and root cause analysis; improve observability, automation, and operational runbooks.

The Sales Engineer will support account teams, conduct product demonstrations, develop technical proposals, and analyze customer data scenarios to promote data observability solutions.

Build and maintain enterprise AI governance components (LLM gateway, Agent Firewall) across full stack (Go backend, React frontend). Ship features, harden security, integrate LLM provider APIs, collaborate with product, partners, and enterprise customers, and contribute to open-source tooling.

Lead full-stack product engineering for AI-powered healthcare features. Build frontend experiences, backend services, APIs, relational data models, and AI workflows. Prototype, ship, and iterate production systems; improve AI via evaluation and product signals. Ensure reliability, privacy, observability, and operational readiness while collaborating with product, design, customers, and engineers.

Lead ownership of the Salesforce platform with emphasis on CPQ and Billing: design and deliver Apex/LWC/Flow solutions, manage integrations (REST/SOAP, platform events, middleware), run CI/CD, enforce development best practices, mentor engineers, and apply AI-assisted tools to accelerate delivery.

Design, build, and scale AI-enabled cloud infrastructure including RAG pipelines, vector DBs, and multi-agent flows. Implement IaC (Terraform/ARM), Kubernetes (AKS/EKS), containerization, automation (Python/PowerShell), cloud security controls, and collaborate with cross-functional teams to deploy production LLM workloads.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead development and execution of AI-specific test plans to evaluate LLM and AI system quality, robustness, security, and compliance with OWASP AI Testing Guide and related standards. Document defects and remediation, capture evidence, validate model behavior (prompt-response, hallucinations), implement AI-driven QE approaches (autonomous test generation, self-healing automation, defect prediction), and report results to stakeholders.

Design and build hands-on curriculum and lab environments teaching technical learners to build, configure, and deploy agentic AI on Veeva's platform. Create working AI agent demos and reference implementations, maintain advanced Java and REST API coding courses, partner with Product and Engineering, and apply instructional design to complex technical content.

Design and build hands-on technical curriculum and lab environments teaching how to build, configure, deploy, and troubleshoot AI agents. Create working agent demos, Java/REST-based exercises, partner with Product and Engineering, and apply instructional design to advanced coding-intensive courses for customers, partners, and internal teams.

Design and deliver hands-on technical curriculum that teaches customers and internal teams how to build, configure, deploy, and troubleshoot agentic AI solutions. Create working agent demos, lab environments, sample code, and instructor-led courses requiring Java and REST API fluency. Partner with Product, Engineering, and Services to shape content, apply instructional design best practices, and keep training current with agent frameworks, tool integrations, prompt design, RAG, orchestration, and evaluation.

Design, build, and deploy AI-powered internal tools and intelligent workflows to automate People, Finance, Recruiting, and operations. Identify improvement opportunities, translate business needs into technical solutions, own end-to-end execution, and collaborate across teams to ship reliable production software and AI integrations.

Design, build, and ship AI-powered internal tools and intelligent workflows to automate operations across People, Finance, Recruiting, and other teams. Own end-to-end execution from discovery to production, integrate AI services, work cross-functionally, and iterate on deployed systems.

Design and build AI-powered internal tools and automations to improve operations across People, Finance, Recruiting, and business workflows. Own end-to-end delivery from discovery to production, integrating APIs, databases, cloud infrastructure, and AI systems while collaborating with cross-functional teams.

Lead design and implementation of cloud-first, AI-integrated full-stack applications on GCP. Drive rapid prototyping and MVPs, mentor developers, define architecture, maintain CI/CD pipelines, and collaborate cross-functionally to deliver production-ready solutions.

As a Senior AI Kernel Engineer, you will design and optimize high-performance kernels for AI inference on GPUs and custom accelerators, improving performance and collaborating with various teams to influence architecture and implementation.

Lead design, build, and operation of GenAI product features and a shared AI platform: RAG pipelines, LLM-driven personalization, conversational agents, agentic workflows, eval frameworks, and MLOps. Own end-to-end production lifecycle, observability, on-call, and integrations with marketing/BI systems. Collaborate with personalization teams and document architectural and evaluation outcomes.