Top AI Engineer Jobs

Build and operate core AI agent infrastructure: orchestration, context management, filesystem and sandboxed execution, model routing and serving, databases, Kubernetes, networking, and cloud systems to make long-running agent workloads fast, reliable, safe, and cost-efficient.

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.

Design and build agentic AI systems for cybersecurity: architect AI cyber taskforce and production-grade agentic components, implement ranking/retrieval/search powered by LLMs, design training/evaluation frameworks, decompose security workflows, address scale/performance, and mentor engineers while contributing to engineering and operational excellence.

Build and ship agent-driven internal products and Slack interfaces, extend and steward the Centaur runtime and agent CLIs, integrate harnesses and developer workflows, and maintain open-source community-facing docs, triage, and PRs to improve firm operations and developer extensibility.

Build and operate the core control plane and durable services coordinating agent runs; develop and harden a sandboxed Kubernetes runtime; manage secrets and secure egress; own reliability, monitoring, incident response, and failure recovery; and maintain self-hosted deployment, configuration, and upgrades for Centaur.

Pioneering machine learning solutions for the defense sector, developing and deploying production-grade ML models, tackling complex data challenges, and collaborating with engineers.

Lead technical evaluations and pre-sales activities for data-driven, AI-powered Salesforce solutions. Design data architectures and AI use cases, deliver demos, proofs of concept, and workshops, advise customers on data ingestion/integration and real-time analytics, and collaborate with sales and marketing to drive revenue.

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.

As a Software Engineer on Applied AI, you will build and operate scalable systems for data generation and model evaluation, collaborating closely with AI researchers and customers to deliver high-impact solutions.

Design, implement, test, and maintain cloud-native software solutions. Troubleshoot complex defects, build APIs and integrations, enable AI/LLM capabilities, mentor junior engineers, contribute to CI/CD and observability, and collaborate with cross-functional teams to deliver reliable systems.

Design, build, and operate large-scale AI infrastructure for training, inference, evaluation, and autonomous agent workloads across cloud, on-prem, hybrid, and edge. Lead architecture, reliability, performance tuning, GitOps/IaC automation, and cross-functional collaboration while mentoring engineers and supporting production incidents and continuous improvement.

Design, build, and operate scalable, secure AI platform infrastructure for training, fine-tuning, inference, evaluation, and autonomous/multi-agent systems. Lead architecture, optimize GPU-accelerated and distributed systems across cloud, on-prem, hybrid and edge, implement IaC/GitOps/CI-CD, integrate model serving technologies, mentor engineers, and participate in production support, performance tuning, and incident response.

Design and build scalable backend services and APIs for HPE EdgeConnect Orchestrator, integrate AI/LLM-driven workflows and Copilot capabilities, work with analytics and telemetry for real-time insights, and ensure quality through testing, debugging, and performance optimization.

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.

Develop infrastructure and tools to integrate Generative AI into enterprise applications. Build scalable end-to-end ML pipelines on heterogeneous hardware, collaborate with data scientists to train/fine-tune models, lead product development for the Generative AI suite, mentor junior engineers, and own production deployments.

Lead design and build of Generative AI infrastructure and tools, deploy large-scale ML/LLM models to production, enable scalable ML pipelines on heterogeneous hardware, collaborate with product and data science, lead cross-team architecture and technical discussions, manage projects, and mentor engineers.

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.

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

Design, orchestrate, and optimize large-scale LLM pre-training runs across 1,000+ GPUs using PyTorch/DeepSpeed/Megatron-LM. Improve networking (InfiniBand/RDMA) and memory management, implement 3D parallelism, and automate checkpointing and failure recovery for month-long training jobs on SLURM or Kubernetes GPU clusters.

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.

Design and orchestrate large-scale LLM pre-training across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM. Optimize InfiniBand/RDMA networking and memory to avoid OOM, automate checkpointing and failure recovery for month-long runs, and manage SLURM or Kubernetes GPU clusters. Implement systems-level improvements using C++, CUDA, and Python.

Design and implement AI platforms (front-end and back-end), collaborate on data preprocessing and model training, deploy AI models to production, build and maintain data pipelines, integrate AI with an Ethereum Layer 2/Web3 stack, and contribute to related company projects with potential to lead a team.

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