Top AI Engineer Jobs

Lead rollout and enablement of an agentic AI platform across departments: deploy and integrate generative AI tools, build cost and usage dashboards, define vendor risk controls, create operational SOPs and training, and bridge IT, security, and engineering to scale safe AI workflows.

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, deploy, and maintain large-scale ML workflows and knowledge graph services. Build production-ready LLM and RAG-based search pipelines, data ingestion/preprocessing, feature stores, monitoring, and security. Collaborate with cross-functional teams, document systems, research advanced ML/NLP techniques, optimize knowledge graph algorithms, and mentor junior engineers.

Design, implement, and optimize AI algorithms and software for computer vision, NLP, recommendation systems and ASR. Develop, test, and maintain models and production-quality code, participate in code reviews and CI/CD workflows, and document algorithms and technical processes.

Design, prototype, and deploy edge AI models onto embedded hardware for secure, disconnected government environments. Build low-latency multi-node communication, quantize and compile multi-agent pipelines, develop asynchronous multimodal fusion and signal-processing algorithms, maintain HIL simulations, and lead ruggedized field assembly, calibration, and testing.

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.

Lead development of generative AI features and agents, scale platform to ingest and query terabytes of data in real time, optimize search and notifications, ship production-grade software across full stack, and collaborate with deployment teams and users to iterate on safe, impactful AI-driven user experiences.

Design, build, and deploy AI/ML models and tools (NLP, RAG, predictive analytics) integrated with PHP/Laravel backends and MySQL. Develop inference and training pipelines, monitor and retrain models, integrate AI features into web platforms, and contribute to long-term AI strategy.

Lead development of generative-AI features and scalable backend systems. Design, ship, and iterate production-grade software that processes terabytes of real-time data, optimizes search/query performance, and integrates ML models to deliver safe, user-focused AI experiences.

Build and deploy agentic, multi-step AI workflows and reusable agent skills; generate production AI images; use AI coding tools daily; develop API integrations (Google Workspace, Notion, Slack, Workable); own projects end-to-end including logging, error handling, and monitoring.

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 AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.