Top Remote AI Engineer Jobs

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

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.

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.

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.

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.

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 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.

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.

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

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.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The Principal Software Engineer will lead the design and build-out of an AI Context Layer, ensuring high-quality data for AI systems, while managing access control and developer interfaces, and leading cross-team projects.

Design and build secure, cloud-native AI applications using LLMs, RAG, and Amazon Bedrock to provide AI-assisted search, summarization, and clinical question-answering; integrate with enterprise healthcare systems using FHIR/HL7, implement vector search and prompt orchestration, ensure Fed security/privacy compliance and CI/CD, and support testing, monitoring, and governance for production readiness.

Lead technical discovery and solution design for enterprise MedTech customers, translate claims data into commercial insights, deliver data via secure sharing/APIs/warehouses, own RFPs and security reviews (SOC2/HIPAA/BAA), enable demo environments and AI narratives, and institutionalize best practices across Sales, Product, Data, and Engineering.

Lead technical architecture and delivery of end-to-end AI solutions across the Workday ecosystem. Design data flows, integrations, security and RAG/LLM patterns; advise executive stakeholders; lead technical workstreams; collaborate with Product and Engineering; ensure privacy, ethical AI, and scalable deployments for complex global customers.

Lead developer advocacy for AI and agentic security: advise security companies, accelerate NVIDIA stack adoption, guide partner integrations, map ecosystems, collaborate cross-functionally, run community events, and feed product teams with field insights.

Design, build, and maintain scalable enterprise full-stack applications using Java, Spring Boot, microservices, and modern front-end frameworks. Implement cloud-based solutions on AWS, contribute to CI/CD and DevOps automation, optimize performance and security, and integrate AI-driven capabilities using Python and orchestration tools like LangChain and LangGraph.

Build and improve production AI systems that extract structured data from legal documents into a household knowledge graph. Own and design evaluation suites, improve agent orchestration and tooling, create human-review workflows, and ship dependable, scalable changes to production while collaborating with domain experts and product.

Lead design and rewrite of cloud-native core systems. Build scalable back-end services and front-end features, drive architecture and testing discussions, mentor engineers, and improve telemetry, performance, and reliability.