1,223 AI Engineer jobs in United States available for remote work from home. Apply for positions such as Senior and/or Principal Software Engineer - AI Frameworks Networking, Systems and Tools, Staff Firmware Engineer, AI Native, Edge ML, AI Field Engineer, AI infrastructure (Remote - US) and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will be responsible for designing, implementing, and ensuring the quality of AI systems stacks and their integration with Azure. The role involves collaborating across disciplines to optimize model training and inference while driving the adoption of robust coding standards.
You will design and build a reusable on-device inference framework to enable edge machine learning across the company's hardware portfolio. Additionally, you will perform core firmware engineering tasks, including driver development and debugging, while integrating ML models into resource-constrained environments.
You will manage the full pre-sales field cycle, including discovery, POC scoping, and model selection for enterprise and AI-native customers. Additionally, you will deploy and fine-tune open-model LLMs directly within customer environments to ensure production-grade performance.
Define and own the technical vision for AI-powered GTM systems, architecting solutions that integrate low-code platforms with custom agentic AI pipelines. Lead architectural design for complex, enterprise-scale workflows while establishing standards and frameworks for the engineering team.
You will own the end-to-end implementation of AI products at automotive dealerships, including system configuration, data validation, and staff training. Additionally, you will act as a feedback loop to the product team and develop repeatable runbooks and SOPs for future deployments.
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You will spearhead AI integration initiatives to enhance care navigation and PBM services while architecting scalable, production-grade full-stack solutions. The role involves leading rapid prototyping cycles and establishing technical standards for AI implementation across the platform.
You will design, develop, and maintain AI platform components such as model serving layers, retrieval infrastructure, and evaluation pipelines. You will also collaborate with product engineering teams to integrate AI capabilities while establishing standards for reliability, safety, and compliance.
You will own the core runtime surfaces of the AI platform, including agent loops, tool execution, and memory management. Additionally, you will collaborate with product teams to integrate domain content and ensure the reliability and observability of the platform in production.
The AI Engineer will develop and operationalize AI and analytics solutions, including predictive models and reporting assets, to support healthcare decision-making. They will also manage vendor relationships, lead testing initiatives, and collaborate with end-users to define business requirements and optimize workflows.
You will own the end-to-end front-end experience for an enterprise-scale Agentic AI platform, designing intuitive interfaces that translate complex AI reasoning into trustworthy experiences. You will collaborate with AI engineers and product teams to build responsive, real-time interfaces that support mission-critical healthcare modernization.
The Senior Applied AI Engineer will design, prototype, and implement production-ready AI capabilities to improve reasoning, accuracy, and developer productivity. They will also optimize autonomous agent workflows and retrieval-augmented generation pipelines while collaborating with cross-functional teams to enhance platform intelligence.
The AI Engineer will implement bounded MOD scenario wrappers within the existing RFDS Agent to automate RF tower design processes. They will also lead the pilot and rollout phases, providing technical support and ensuring the system evolves toward autonomous execution.
You will lead the design, build, and deployment of AI-powered solutions while reengineering business processes to integrate advanced technologies. Additionally, you will facilitate AI adoption through training, stakeholder engagement, and the creation of reusable AI assets.
Design and build production-grade AI applications using LLMs, RAG, and agentic workflows. Manage full-stack development, cloud deployment, and integration of AI models into enterprise systems.
Design, build, and deploy production-grade AI agents and end-to-end workflows to solve business problems. Collaborate with cross-functional teams to integrate LLMs with internal systems while optimizing for reliability, performance, and cost.
You will own features end-to-end, from design through implementation and rollout, while collaborating with product and engineering teams. Additionally, you will extend agentic AI foundations into customer-facing product experiences across the company's suite.
You will architect and build end-to-end Generative AI applications, including document intelligence and RAG pipelines, on Google Cloud. Additionally, you will collaborate with clients to translate business needs into scalable, high-performance AI architectures while applying MLOps best practices.
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The engineer will build features and solutions within a pod, working directly with business users to implement AI-driven applications. They are responsible for maintaining high code quality through testing and evaluations while following design direction from the Solution Architect.
Design and evaluate complex CFD and aerodynamics tasks to train AI models in engineering reasoning. Develop deterministic scoring checkers and verified reference solutions to ensure technical accuracy of AI-generated responses.
The Principal Software Engineer will drive cross-team architectural decisions for the AI SIEM platform, ensuring seamless integration between data ingest, analyst workflows, and detection engines. They will mentor senior engineers and establish technical standards to maintain system interoperability and performance at scale.
The AI Risk Engineer will design and implement security controls for AI and LLM-based applications while building threat models to mitigate risks like prompt injection and model abuse. They will also collaborate with security and compliance teams to conduct AI security assessments and drive remediation efforts.
Build, scale, and maintain biosecurity evaluations to measure dual-use biology capabilities in frontier AI systems. Develop internal tools and cloud infrastructure to enable large-scale capability assessments and public trend analysis.
Design, develop, and deploy scalable AI and machine learning models to enhance automation and analytics within the leave management platform. Collaborate with cross-functional teams to implement MLOps best practices and ensure ethical AI standards are maintained.
You will own features and systems end-to-end for the Corporate Insight Agent, from requirements gathering through deployment and iteration. You will also serve as a subject matter expert across the stack, collaborating with product management to shape requirements and drive technical initiatives.
The role involves embedding within client operations to learn business processes firsthand before rebuilding them using GenAI and agentic systems. You will be responsible for shipping production-ready AI applications and owning the measurable business outcomes for clients.
Build and ship mobile features for KIRA's AI Finance Agent across Android and iOS platforms. Collaborate with product and design teams to create intuitive flows for insurance, payments, and financial services.
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Build and ship mobile features for KIRA's AI Finance Agent across Android and iOS platforms. Collaborate with product and design teams to create intuitive flows for insurance, payments, and financial services.
Build and ship native Android features for an AI Neobank app using Kotlin and Jetpack Compose. Collaborate with product and design teams to optimize user journeys and improve app performance and reliability.
Build and ship native Android features for an AI Neobank app using Kotlin and Jetpack Compose. Collaborate with product and design teams to optimize user journeys and integrate backend APIs for financial services.
The role involves embedding within client operations to learn business processes firsthand before rebuilding them using GenAI and agentic systems. You will be responsible for shipping production-ready AI applications and owning the measurable business outcomes for clients.
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