2,893 AI Engineer jobs available for remote work from home. Apply for positions such as Junior Fullstack Engineer (m/w/d) TS, React, Next.js & AI 100% Remote, Arquitecto/a Big Data Engineer / Architect – Databricks & Mosaic AI, AI SWE / Agentic SDLC Workflow Engineer - T Cloud Public (REF5735F) and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will develop end-to-end features using React, Next.js, and Node.js while integrating AI-driven functionalities like document analysis and agentic workflows. Additionally, you will ensure platform scalability for a growing user base and contribute to architectural decisions and engineering standards.
The candidate will design, evolve, and implement advanced data solutions using Databricks. A primary focus will be on developing Generative AI initiatives leveraging the Mosaic AI platform.
The role involves industrializing AI-assisted engineering workflows by creating reusable patterns for codebase intake, dependency extraction, and documentation generation. You will integrate these AI workflows with existing CI/CD systems, Git platforms, and architecture evidence repositories to ensure auditable and scalable engineering outputs.
The role involves strengthening software handover readiness by utilizing AI-assisted engineering to perform code quality, security validation, and dependency analysis. You will collaborate with cross-functional teams to generate evidence packs and ensure that platform automation and services meet enterprise assurance standards.
The role focuses on designing and launching AI-enabled workflow solutions for People Operations and HR use cases. It involves bridging the gap between business stakeholders and technical teams through requirements shaping and execution planning.
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Design and implement the user interface layer for Netflix's AI Platform to accelerate model creation, evaluation, and deployment. Collaborate with researchers and data scientists to build scalable ML infrastructure and gather requirements for various ML use cases.
Develop evaluation criteria and review system logs to improve the reliability and code quality of advanced AI systems. Refactor code and test for vulnerabilities to ensure AI models generate reliable and secure outputs.
The role involves improving advanced AI systems by evaluating outputs, refining logic, and developing verifiable performance rubrics. You will also refactor code, optimize system behavior, and test for vulnerabilities to enhance AI-generated code quality.
Develop evaluation criteria and review system logs to improve the reliability and quality of AI-generated code. Refactor code, optimize system behavior, and test for vulnerabilities to enhance large language model performance.
Develop evaluation criteria and review system logs to improve the reliability and code quality of advanced AI systems. Refactor code and test for vulnerabilities to ensure AI models generate reliable and secure outputs.
Develop evaluation criteria and review system logs to improve the reliability and code quality of advanced AI systems. Refactor code and test for vulnerabilities to ensure AI models generate reliable and secure outputs.
Develop evaluation criteria and review system logs to improve the reliability and quality of AI-generated code. Refactor code and test systems for vulnerabilities to enhance how AI models optimize software performance.
Design and implement a self-serve user interface layer for Netflix's Machine Learning Platform to accelerate model creation and deployment. Collaborate with researchers and data scientists to evolve Metaflow and improve personalization model development.
Design and build a next-generation ML data and feature platform to improve productivity for ML practitioners. This includes developing near-real-time feature computation engines and centralized feature and embedding stores for high-throughput training and low-latency inference.
Design and deliver multi-petabyte storage systems and high-performance parallel filesystems optimized for AI training and inference workloads. Build Kubernetes-native storage operators and optimize end-to-end data paths to achieve high throughput and low latency.
Build and maintain scalable full-stack applications and APIs using TypeScript, Node.js, and React for a mental healthcare platform. Own features from concept to production while collaborating with cross-functional teams to improve system performance and reliability.
The role involves integrating and optimizing low-level kernel infrastructure for RISC-V processors while leading the development of a native agentic AI software stack. You will collaborate with CPU architects and compiler engineers to ensure software capabilities align with RISC-V architectural features.
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The DevOps Engineer Professional is responsible for designing, developing, and supporting ICT components and applications for the AI Factory Cloud platform. They will also mentor co-workers and consult on innovative technologies to enhance project deliverables.
Design and implement security, identity, and governance controls for AI, virtual desktop, and Microsoft 365 services in a regulated environment. Collaborate with compliance teams to establish security patterns and conduct risk assessments with stakeholders.
You will build and operate the infrastructure and platforms that power AI products, including model training, deployment, and inference. You will also collaborate with researchers and engineers to optimize system performance, scalability, and cost-efficiency.
Design and implement scalable microservices using Java and Spring Boot while leveraging AI coding agents to increase development velocity. Collaborate across cross-functional teams in an Agile environment to deliver end-to-end features for a commerce platform.
The role involves leading AI solutioning for pre-sales, designing scalable GenAI architectures, and translating complex business challenges into value-driven technical solutions. Additionally, the candidate will serve as a subject matter expert, mentor teams, and develop thought leadership content to support organizational growth.
Design and develop scalable AI-driven applications using .NET and Python while leveraging LLMs and Agentic AI architectures. Collaborate with cross-functional teams to implement RAG solutions and ensure enterprise-grade security, governance, and performance.
You will design, develop, and deploy production-ready machine learning and generative AI solutions, including autonomous agent systems. The role involves building econometric models and integrating LLMs with existing APIs and data sources.
Design and implement end-to-end AI solutions, including intelligent agents and automated workflows using LLMs. Collaborate with cross-functional teams to define AI use cases, metrics, and ensure high-performance, scalable production environments.
The VP of AI Engineering will define and lead the enterprise strategy for data and generative AI, overseeing the design and operation of AI-ready data platforms. This role involves managing cross-functional AI initiatives, ensuring regulatory compliance, and driving measurable business value through advanced analytics and LLM integration.
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Oversee the design, development, and operation of large-scale AI infrastructure, including distributed training and GPU resource management. Partner with cross-functional teams to deliver AI-powered products while establishing enterprise standards for responsible AI and model governance.
The Senior Director will provide strategic guidance to software engineering leaders on AI integration, value measurement, and roadmap planning. They will also produce high-impact research, deliver presentations at industry events, and support sales efforts by acting as a market expert.
The trainer will lead a 10-week intensive workforce development program, delivering live instruction on AI engineering, agentic AI, and software modernization. They will facilitate hands-on labs, provide individualized coaching, and assess participant progress through projects and technical demonstrations.
Lead and develop an engineering team while guiding the architecture and technical strategy for production AI systems. Partner with cross-functional stakeholders to translate AI roadmaps into measurable outcomes and ensure compliance with healthcare regulations.
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