Top Remote AI Engineer Jobs

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.

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.

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.

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.

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.

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.

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, 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.

Own and evolve the AWS-based platform powering product and AI workloads. Design IaC (Pulumi), build CI/CD pipelines, manage observability, respond to incidents, support LLM workflows (Bedrock, SageMaker), and partner with engineering and ML teams to improve reliability, scalability, and developer experience.

The Senior AI NodeJS Engineer develops AI Agentic solutions, collaborating with teams, managing tasks, ensuring quality code, and staying updated on AI technologies.

The AI Sales Engineer will build relationships with customers, lead product demonstrations, handle objections, and act as a domain expert in ML. Responsibilities include collaborating with sales and technical teams to create value propositions and developing educational materials about ML and MLOps.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Develop and fine-tune vision-centric large language models and visual encoders, build evaluation benchmarks, collect and prepare visual training data (including for RLHF), prototype architectures for production VLMs, publish research, and mentor junior researchers within a small applied ML research team.

As an AI Platform Backend Engineer, you'll design and deploy backend systems utilizing AI for real-world applications, optimize performance, and mentor junior engineers while working directly with clients to address complex challenges.

As an AI Platform Engineer, you will design and develop AI-driven backend solutions, optimize performance, and collaborate with customers and teams.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Own and evolve Syndi, an agent runtime powering in-product chat and integrations. Design, ship, and operate core runtime surfaces (agent loop, tool execution, memory, context), build eval systems, ensure observability and reliability, review consumer integrations, and implement compliance and legal requirements for production LLM systems.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Owner/implementer of end-to-end full stack features for data-intensive AI products: design and build scalable Node.js backends, REST APIs, and React+TypeScript frontends; optimize performance; establish testing, observability, and CI/CD; collaborate with product, design, and ML teams; debug and support production systems.