Top AI Engineer Jobs

Design Verification Engineer responsible for verifying Tensix IP, building reusable SystemVerilog/Python testbenches and DV infrastructure, developing constrained-random and directed tests, closing coverage, debugging RTL/testbench/workload issues, and integrating CI/CD, simulation/emulation, and containerized regression systems. Collaborates with design, firmware, compiler, and architecture teams.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

Design, develop, deploy, and maintain AI/ML models and MLOps pipelines; optimize model performance; run experiments; integrate models into production systems; collaborate with cross-functional teams to translate ML research into mission value.

Lead and manage an ML Models-as-a-Service team to define vision, build and deploy production ML models and APIs, establish MLOps and CI/CD pipelines, enforce governance and security, collaborate with stakeholders, and oversee project delivery and resource allocation across the model lifecycle.

Design and build end-to-end AI-native solutions including backend agent orchestration and production-ready frontends. Develop GenAI systems using LLMs, autonomous agents, vector search, and RAG. Rapidly prototype MVPs, deploy cloud-native architectures, and create accessible web interfaces and dashboards for non-technical users.

Design and implement agentic AI integrations: build retrieval and ingestion pipelines, embeddings and vector-store indexing, develop APIs/microservices, optimize query routing and caching, implement auth/audit, and support prototypes, demos, and pilot deployments.

Design, develop, deploy, and maintain production ML models and MLOps pipelines. Build custom AI/ML algorithms, run experiments, evaluate and optimize model performance, and integrate models with cross-functional teams for mission-driven applications in a federal environment.

Design, develop, deploy, and maintain production ML models and pipelines. Build MLOps frameworks, run experiments, evaluate and fine-tune models, and integrate AI/ML solutions with cross-functional teams to deliver mission value for federal clients.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Design, evaluate, deploy, and integrate LLM-based and agentic AI systems across SpaceX. Own end-to-end lifecycle from prototype to production, build evaluation and observability frameworks, architect reliable agentic and retrieval-enabled workflows, and collaborate with product, engineering, and operations teams.

Design, build, and productionize AI-powered applications across SpaceX. Own end-to-end lifecycle for LLM and agentic systems: prototype, evaluate, deploy, monitor, and iterate. Architect reliable, safe agentic workflows, advanced retrieval, and observability; collaborate with Product and Operations to deliver production-grade AI features.

Design, deploy, and maintain production AI systems (LLMs and agentic workflows) across SpaceX. Own end-to-end lifecycle from prototype to production, build evaluation and observability, architect reliable agentic and retrieval-based applications, and collaborate with Product, Engineering, and Operations.

Design, evaluate, deploy, and operate production-grade AI systems (LLMs and agentic workflows) across SpaceX. Own end-to-end lifecycle from prototype to production, build evaluation and observability, architect reliable and safe AI-powered applications, and partner with Product, Engineering, and Operations teams.

Design, evaluate, deploy, and integrate AI systems (LLMs and agentic systems) across SpaceX. Own end-to-end lifecycle from prototype to production, architect reliable agentic workflows and retrieval systems, build evaluation and observability frameworks, partner with product and operations, and deliver production-grade full-stack software to accelerate launches and Starlink operations.

Design, evaluate, deploy, and integrate LLM-based and agentic AI systems for production use across SpaceX. Own end-to-end AI lifecycle from prototype to production, build robust evaluation and observability frameworks, architect reliable agentic and retrieval-enabled workflows, and partner with product and operations to deliver safe, production-grade AI applications.

Design, build, and deploy production-grade AI systems (LLMs, agentic workflows) across SpaceX. Own end-to-end lifecycle: prototyping, evaluation, deployment, monitoring, and iteration, partnering with product and engineering to ensure reliability, safety, and observability.

Build, operate, and secure the firm's AI infrastructure and developer tooling to enable Python-based AI applications. Implement Azure AI Foundry, integrate LLM APIs (Anthropic, Azure OpenAI), design data pipelines (ADF, Databricks), manage cloud infrastructure (IaC, Kubernetes), and enforce security, governance, and compliance for regulated financial environments.

Build and own core backend infrastructure for long-running agentic AI workflows, ensuring high reliability, observability, disaster recovery, secure execution sandboxes, HIPAA-compliant data handling, and production-grade IAM and secrets management.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and scale backend services, platform infrastructure (serverless), and APIs powering financial workflows for clinical development. Integrate with LLM agents and AI workflows, implement frontend components as needed, resolve production incidents, contribute to architecture, and mentor teammates.

Design, prototype, and validate high-voltage DC power conversion and distribution systems (including 800V-class). Develop gate drivers, protection/sensing, and perform SPICE-based simulation, PCB/layout optimization, and high-power lab testing. Integrate with embedded control platforms and collaborate across thermal, mechanical, and system architecture teams to deliver validated hardware quickly.

Build and scale core AI-native healthcare products across the stack, focusing on real-time audio processing, NLU/transcription, clinical documentation, EHR integrations, and distributed production systems. Drive architecture, ship features with high ownership, and operate in a fast-moving, regulated environment.

The Senior Backend Engineer will build and operate backend systems for AI agents, focusing on reliability, API design, and data modeling, while enhancing code quality and performance in a fast-paced environment.