Top Remote AI Engineer Jobs

Design, build, deploy, and operate AI/ML capabilities in AWS GovCloud using FedRAMP-authorized services. Responsibilities include data preparation, model development, evaluation, deployment, monitoring, RAG implementations, embedding/vector search integration, Python development of production services, containerized deployments, CI/CD, and collaboration with engineering and cybersecurity teams to meet mission and compliance requirements.

Design and optimize AI communication middleware for distributed multi-node, multi-GPU training. Implement C/C++ performance-critical features, improve GPU communication paths, profile and tune frameworks (PyTorch, TensorFlow, JAX, DeepSpeed), develop benchmarks, contribute upstream, and collaborate with kernel, driver, switch, and systems teams to validate and deploy high-performance networking for AI/HPC.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

Lead operations for large-scale AI infrastructure: manage NVIDIA GPU and high-performance networking environments, troubleshoot Linux/Kubernetes/storage/hardware issues, drive incident response and root cause analysis, improve observability and automation, mentor engineers, and collaborate with engineering and datacenter teams to ensure platform reliability and scalability.

Lead the AI engineering org for a YC-experienced startup: drive 0->1 product development, design full-stack architectures, partner with customers and product, mentor engineers, and establish scalable production AI systems and technical strategy.

Design, develop, and upstream-enable AI/HPC communication middleware (MPI/SHMEM, NCCL/RCCL). Integrate middleware with transports (libfabric/UCX/verbs), optimize performance, collaborate across kernel/driver and switch teams, analyze performance traces, reproduce and fix customer issues, and contribute to open-source projects.

Develop and optimize AI/HPC communication middleware (MPI/SHMEM, CCL/NCCL/RCCL) over Cornelis interconnects; integrate with transports (libfabric/OFI, UCX), contribute upstream open-source, collaborate across kernel/driver and switch teams, and analyze performance traces to reproduce and fix customer issues.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Lead development and adoption of AI-driven and traditional test automation, maintain UI automation frameworks, own performance/load testing strategy, align testing to architecture and risk, mentor QA engineers, and collaborate with product and engineering to improve quality and testing efficiency.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.

The Software Engineer will develop backend systems for an AI Energy Agent, including data integration, microservices, and scalable architecture, focusing on performance and reliability.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Lead architecture and evolution of Juniper Square’s distributed services, cloud infrastructure, and developer platform. Design scalable, multi-tenant, event-driven systems; build paved-road developer tooling, CI/CD, and observability. Hands-on coding, prototyping, and performance troubleshooting. Enable production AI/LLM workloads, ensure security/compliance, support enterprise upmarket needs, and mentor engineering teams to raise operational excellence and reliability.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Design and implement AI-driven applications using Verint technology, working with cross-functional teams to enhance customer experience and operational efficiency.

As a Principal Software Engineer, you will architect AI-enabled systems, mentor teams, and guide technical decisions for scalable products.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

Design, build, test, deploy, and maintain cloud-based applications, microservices, and infrastructure. Ensure application performance and quality, fix bugs, write automated tests, and collaborate on new features. Use AI-assisted development tools daily and implement IaC and modern architecture patterns to deliver secure, maintainable code.

Develop and enhance user-facing features for an LLM observability platform using React and TypeScript. Build reusable components and front-end libraries, translate designs into performant UI code, optimize cross-device/browser performance, and collaborate with backend engineers and designers to improve usability.

Design, build, and operate secure enterprise AI platform capabilities connecting models, agents, and internal systems. Own major infrastructure workstreams (MCP gateway, enterprise context), build backend services and APIs, enable identity-aware governance (access, permissions, auditing), design agentic workflows, and take operational ownership while partnering with stakeholders to scale dependable AI tooling across Spotify.

Work with customers to discover high-value AI use cases, design and prototype LLM- and agent-based solutions, build demos and pilots, and support transition to production. Provide technical architecture, documentation, training, governance guidance, and hands-on implementation support across integrations, automation, and evaluation to enable secure, explainable, and measurable AI adoption.

The Applied AI Software Engineer will evaluate LLM-based agents, manage benchmarking, and ensure performance, safety, and reliability in healthcare settings. Responsibilities include designing evaluation plans, running experiments, collaborating with teams, and developing internal tooling for AI features.

Build, productionize, and operate AI-driven features and integrations across frontend, backend, and model layers. Prototype and deliver agent-driven experiences (conversational interfaces, RAG, tool-calling), integrate LLMs and external data sources, and collaborate with product and design. Participate in on-call/standby duties.

Design, build, and operate scalable backend services and internal APIs for an AI platform. Integrate LLMs and AI tools into production workflows, ensure platform reliability via observability and incident response, optimize AI runtime performance and cost, and contribute to shared infra like CI/CD and deployments.