Top Remote AI Engineer Jobs

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Design, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

Lead integration of AI/ML into a cloud-native human-performance platform: architect generative AI and RAG workflows, build serverless backends and microservices, optimize data pipelines (SQL/NoSQL, embeddings), maintain CI/CD, modernize legacy PHP backends, mentor engineers, and drive architecture and deployment of LLM-based features.

The Senior Solutions Engineer will design and implement infrastructure for AI and HPC workloads, engage with customers, and lead technical discovery and architecture design.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Lead design, build, and scaling of Agentic AI and industry-specific solution accelerators on Genesys Cloud. Develop reusable multi-agent workflows, RAG-based knowledge orchestration, integrations with enterprise systems, prototypes, and production-grade AI applications. Provide technical leadership, mentor engineers, collaborate with product and industry SMEs, and deliver repeatable solution blueprints to accelerate deployment and time-to-value.

Designs, builds, and operates AI-powered solutions for go-to-market teams, including retrieval, context, evaluation, guardrails, agent workflows, and operational automation. Owns problems end to end from discovery through launch, measurement, and iteration while shaping data and system architecture. Establishes reusable AI tooling, improves engineering velocity through AI-augmented development, mentors engineers, and maintains high standards for reliability, privacy, security, observability, and customer trust.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Build and maintain responsive web and mobile interfaces with React.js or React Native while integrating AI and LLM capabilities. Develop prompts, AI agents, copilots, chatbots, RAG workflows, and intelligent assistants using Python and agent frameworks. Collaborate with product, UX, and backend teams, contribute to architecture and code reviews, optimize performance, and evaluate emerging UI and AI technologies.

The AI Developer will develop, test, and deliver NICE CXone AI solutions, focusing on chat bots and other AI systems, while also supporting production and development teams.

Build integrations that discover, identify, and govern AI agents across third-party platforms such as Salesforce Agentforce, Microsoft, AWS, and Google Cloud. Retrieve native identity signals, integrate them with Delinea’s identity layer, and govern agents through provider APIs and attestation. Improve shared discovery capabilities while using connector patterns, OAuth, token exchange, and event-based ingestion. The role requires substantial backend engineering experience, strong integration skills, and familiarity with cloud, AI agent, security, or identity ecosystems.

Build scalable customer intelligence workflows using Python, SQL, automation, product usage analytics, customer health modeling, and applied AI. Develop Slack summaries, data pipelines, renewal prediction models, and LLM-driven synthesis workflows. Partner with Customer Success and Analytics Engineering to identify customer risk, expansion, and adoption signals, improve health scoring, and deliver actionable insights. The role is remote-first and intended for an early-career engineering or analytics professional.

Build and operate production AI agent features for Plane, including agent workflows, MCP servers, retrieval systems, customer-facing interfaces, evaluation harnesses, tracing, permissions, and audit models. The role requires shipping reliable LLM-backed features to real users, managing tool calling and failure recovery, measuring quality and performance, and partnering with engineering and product teams to deliver production surfaces.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Develop, test, deploy, and maintain .NET-centric web applications using C#, Angular 17+, and Azure. Build and consume REST APIs, work with SQL Server, source control, CI/CD pipelines, and collaborate on code reviews, debugging, performance optimization, and third-party integrations.

Develop and operate a system for argumentation related to AI safety, utilizing ontologies and knowledge graphs while collaborating with various stakeholders.

The Research Engineer will develop AI platforms focusing on alignment challenges, multi-agent systems, and optimization algorithms while collaborating on technical publications and prototypes.

The Software Engineer - AI Systems will design and implement systems to coordinate LLMs, build testable software stacks, conduct research, and collaborate with team members to create AI-powered security solutions.

Develop and optimize AI models and training systems for robotics applications, focusing on design, implementation, and performance optimization of ML models.

The Partner AI Deployment Engineer will guide AWS engagements, shape technical strategy, build AI architectures, and mentor partners to drive successful AI deployments.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.