Top Remote AI Engineer Jobs

The Staff Engineer will architect and build AI and ML infrastructures, mentor engineers, and lead high-impact initiatives to enhance ML capabilities across Attentive's platform.

The Lead Product Engineer will design and deploy enterprise applications, drive Azure migrations, and collaborate with cross-functional teams in an AI-driven environment, mentoring junior engineers and implementing best practices.

The role involves building and integrating full-stack features for an AI chat application, focusing on reliable workflows and real-time AI interactions.

The Staff Software Engineer will design and develop scalable cloud services, focusing on AI infrastructure, with hands-on system architecture, mentoring, and engineering culture shaping responsibilities.

Develop AI/ML-powered features in ClickHouse Cloud, focusing on frontend and API architecture, UI/UX, and integrations with AI systems.

Own and evolve ClickHouse's Python connector and SDKs; build and maintain integrations with orchestration and transformation tools; design AI/LLM integration patterns (RAG, embeddings, feature stores); engage with open-source community; collaborate with Product and Cloud teams to prioritize and ship production-grade data integrations.

Lead strategic enterprise support for high-value customers, resolve critical escalations across CX platforms, drive AI-augmented support practices, mentor engineers, influence product direction, and ensure customer adoption, retention, and long-term success.

Lead architecture and build cloud-native, resilient enterprise software and Agentic/Generative AI solutions for Claims Adjudication. Design microservices, APIs, CI/CD and observability; integrate LLMs, vector stores, and orchestration frameworks; ensure AI governance and production reliability while mentoring teams and aligning technical strategy with business outcomes.

Partner with customers to architect, build, and deploy high-scale production AI applications. Translate ambiguous goals into reliable, observable services, implement end-to-end model deployment and monitoring, optimize ML projects, and own customer-facing technical delivery and product outcomes.

Build and scale developer platform and AI-powered tooling to improve developer velocity, CI/CD observability, and Kubernetes reliability. Implement agentic coding/review workflows using LLMs, create self-service templates and automation, contribute to incident playbooks, and drive adoption via docs and enablement.

Lead design, development, testing, and App Store submission of production-scale iOS features in Swift. Collaborate cross-functionally, enforce code quality via reviews and refactoring, maintain technical documentation, and adopt AI-assisted tooling to improve productivity across large, user-facing modules.

As a Senior Applied AI Engineer at Vanta, you'll design and implement AI-powered features, mentor engineers, and ensure AI systems improve customer experience, leveraging LLMs and full-stack development skills.

Design, develop, test, deploy, and maintain production software with emphasis on AI workflows, performance, security, and mentoring junior engineers. Collaborate in Agile processes and build testing suites and CI/CD pipelines.

Design and run full-spectrum red team operations across corporate, lab, OT, cloud, and AI environments. Develop offensive toolchains, build scripts and exploits, execute end-to-end attack chains, emulate adversaries based on threat intel, and collaborate with IR, threat intel, and detection engineering to convert findings into durable detections and defenses.

Serve as an SRE-focused AI enablement partner: evaluate AI architectures, coach engineering teams on LLM integrations and agentic/RAG patterns, advise on observability, SLOs, governance, and operational readiness, and develop internal standards and documentation.

12-16 week internship implementing and optimizing GPU/accelerator kernels and custom operators, profiling and improving LLM inference and runtime performance, building benchmarks, fixing regressions, and shipping code to production or open-source AI infra projects.

Operate and optimize large-scale LLM pre-training on 1,000+ GPU clusters using PyTorch, DeepSpeed, or Megatron-LM. Improve networking (InfiniBand/RDMA), memory management, checkpointing, and failure recovery. Manage SLURM/Kubernetes GPU clusters and apply systems engineering (C++, CUDA, Python) and 3D parallelism techniques.

Manage model registries, continuous training loops, and A/B testing infrastructure. Deploy agentic models as scalable microservices on Kubernetes and build observability dashboards to track token usage, latency, and agent reasoning paths. Maintain MLOps pipelines and ensure scalable, observable AI infrastructure.

Lead development of an AI-enabled Ethereum Layer 2 platform: build front-end and back-end systems, deploy and optimize AI models, create data pipelines, collaborate on model training, and contribute to product innovation and other company projects.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Design, prototype, and integrate generative AI solutions and agents across products. Collaborate with cross-functional teams to deploy, test, and optimize AI workflows, educate developers, and engage with the enterprise systems management community through webinars and content.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

Senior full‑stack engineer responsible for building production-quality Salesforce platform capabilities, integrations (MuleSoft, REST/SOAP), AI-enabled tooling (Claude plugins, MCP servers), CI/CD, and reusable libraries. Mentor engineers, run architecture/code reviews, and collaborate cross-functionally to deliver scalable, secure cloud-native solutions using Python, LWC, Apex, and AWS.

Design and implement AI-powered features for healthcare platforms, develop full-stack applications, and collaborate with stakeholders in a fast-paced Agile environment.