Top AI Engineer Jobs

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.

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.

Design, develop, and maintain enterprise full‑stack applications; implement AI/ML pipelines and RAG systems; ensure DOE compliance and hardened security; collaborate with infrastructure and security teams; support CI/CD, containerization, and production incident response.

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 design and implementation of AI-first testing frameworks and automation to validate LLM integrations and agentic Product Led Growth workflows. Perform white-box testing, identify and fix quality gaps, implement human-in-the-loop model evaluation, and collaborate cross-functionally to integrate quality across the agile delivery lifecycle.

Support DISA by researching and applying AI technologies (ML, NLP, CV), developing and evaluating models, and aiding proofs-of-concept. Lead technical planning, quality/configuration management, produce documentation and briefings, and coordinate stakeholders to support RMF/STIG cybersecurity initiatives and mission outcomes.

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.

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.

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.

Build agentic, reasoning-driven systems on top of LLMs; own full-stack AI product features from prompt design to orchestration, tooling, monitoring, and UX. Prototype with design, run LLM evaluation and A/B tests, integrate emerging methods, and collaborate directly with founders and customers to deliver product-facing AI features.

The Cloud & AI Infrastructure Engineer designs and optimizes Azure infrastructure, develops CI/CD pipelines, and ensures reliability and security for AI applications.

The Senior AI/ML Engineer will build scalable AI systems, work with LLMs, optimize models, and deploy solutions using AWS while ensuring ethical standards.

The role involves managing enterprise AI platforms, supporting IT operations, security initiatives, and enhancing AI tooling efficiency through proactive support and coordination.

As a Senior Product Engineer, you will own end-to-end feature development on customer-facing products, make product decisions, and work closely with a collaborative team.