Top Artificial Intelligence Jobs

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.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Design and build AI-enabled robotics proofs of concept, then deploy and integrate them in customer sites. Responsibilities include camera placement and calibration, model deployment, I/O configuration, on-site troubleshooting, and customer training. Collaborate with sales and engineering teams, produce documentation and integration guides, and feed deployment learnings back into product development.

Build and integrate AI agents and automation workflows, extend full-stack apps with LLMs, manage data pipelines (Snowflake, Postgres), support server-side event tracking, analytics and dashboards, ensure secure HIPAA/SOC2-compliant handling of PII, and collaborate with leadership to deploy AI-driven marketing and compliance solutions.

Lead PJT's AI security and risk program: own AI risk framework and policies, evaluate vendors and internal AI deployments, perform AI threat modeling, embed AI security controls, monitor regulation and report to leadership, support audits, and deliver awareness training. Collaborate with Legal, Compliance, Procurement, and Technology to manage model, data, and third-party AI risks across the firm.

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.

Work with senior leadership to drive firm-wide AI adoption: study team workflows, identify high-leverage AI use cases, design and deliver training, recruit internal evangelists, evaluate tools/vendors, track adoption, and scale best practices across global offices.

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.

Administer, secure, and support enterprise AI platforms (Microsoft Copilot, Claude, Azure-hosted solutions). Manage integrations, access controls, RBAC, audit logging, cost monitoring, and Tier 2/3 incident resolution. Coordinate vendors, maintain documentation, configure Azure environments, and ensure compliance with firm governance and data protection policies. Provide occasional after-hours and training-related travel support.

Own Halo end-to-end: define roadmap, shape the AI-native consumer experience, coordinate advisor handoffs, and work with engineering and data science to ship multi-agent, compliance-grade AI features. Measure success across a coupled consumer-advisor product, prioritize ruthlessly with limited resources, and embed SEC-compliant traceability into product decisions.

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 design, evaluation, and productionalization of AI features (LLMs, deep learning, RL). Build AI infrastructure, ensure model observability, monitoring, and reliability, and lead an applied AI team to integrate trusted, explainable capabilities into product workflows.

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.

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.

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.

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 ship internal AI frameworks and staff-facing tools for care navigators: design the Navigator Workbench, optimize data feeds and transcripts, define prompts/decision trees and safety guardrails, integrate with engineering and data architecture, monitor AI output quality, and enable non-technical ops to extend systems.

Lead and grow FAS's AI policy portfolio by setting strategy, managing staff and budgets, overseeing grant-funded projects, building cross-sector partnerships, fundraising, and advising policymakers on AI safety, fairness, responsible adoption, and AI for public missions.

Lead development of production AI systems for building-system design including vision pipelines for PDF/CAD parsing, LLM+RAG workflows for code-compliant designs, AI agent orchestration, and full-stack features across a Typescript/React/Node/Postgres platform. Significant ownership and technical direction at an early-stage startup.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

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, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Lead enterprise AI strategy and governance, advise the CEO and exec team, establish AI policies and guardrails, prioritize and guide high-value use cases, oversee PoCs to scaled deployments, deliver AI literacy across departments, ensure compliance and responsible AI practices, and enable business leaders to safely adopt AI.