Top AI Engineer Jobs

Design and release precision mechanical hardware, housings, assemblies, tooling, fixtures, and alignment systems for LiDAR sensors. Create manufacturing-ready drawings using GD&T, perform tolerance analyses, manage engineering changes and BOMs, support optical assembly, collaborate with suppliers and cross-functional engineers, and iterate prototypes toward production.

Build and deploy production-grade AI solutions that improve advisor effectiveness and automate business workflows. Responsibilities include full-stack and backend engineering, LLM and generative AI implementation, prompt engineering, data platform development, distributed systems, cloud-native architecture, Kubernetes deployments, security, scalability, and cross-functional initiative leadership from concept through production.

Partner with enterprise customers to design, prototype, and deploy production Generative AI applications using OpenAI APIs. Provide technical guidance, build prototypes, design enterprise architectures, manage stakeholder relationships, scale best practices, contribute developer resources, and relay product feedback to internal teams.

Develop customer-centric AI features for accounting software using Python and modern AI frameworks. Build intelligent document processing, automated workflows, and AI-powered analytics while collaborating with customers, product managers, designers, and researchers. Lead complex technical projects, apply full-stack development and Agile/XP practices, and support scalable, secure AI solutions across products. The role requires hands-on LLM or AI experience, expertise with embeddings and vector databases, and strong stakeholder communication.

Build AI infrastructure for long-running agents, including durable workflows, context delivery, agent communication, sandbox environments, and machine fleets. Design scalable distributed systems, data models, and real-time infrastructure supporting LLM workloads. Own infrastructure projects end to end, improve reliability and performance, and take systems from prototype to production. Collaborate across the software lifecycle to enable agents to execute and complete complex work.

Build and deploy AI and machine learning solutions for clients, moving rapidly from prototypes to scalable production systems. Responsibilities include generative AI, AI agents, retrieval-augmented generation, model evaluation, enterprise integration, software engineering, cloud deployment, testing, monitoring, and communicating technical recommendations to business and technical stakeholders.

Build and operate scalable AI platform infrastructure enabling secure, reliable access to foundation models and AI agents. Responsibilities include developing model gateways, provider integrations, routing, usage controls, observability, governance, and self-service platform capabilities. The role involves architecting distributed systems, improving reliability and security, collaborating across engineering and product teams, mentoring engineers, and establishing high-quality engineering practices.

Staff Engineer responsible for architecting and implementing secure, scalable full-stack cloud services processing petabytes of data. The role provides technical direction across frontend, backend, and data layers; resolves complex cross-organizational challenges; establishes engineering standards for performance, reliability, and security; aligns technical execution with product strategy; and mentors senior and lead engineers. The position requires extensive experience with cloud-native distributed systems, architecture, technical leadership, cross-team influence, and large-scale reliability.

Design, develop, and deploy production AI/LLM applications and inference pipelines for a large enterprise Linux codebase. Integrate LLMs, vector databases, APIs, and RAG workflows; optimize model performance and scalability; implement monitoring, testing, and developer tooling; collaborate with engineers and researchers to productionize AI prototypes and modernize legacy C/C++ systems.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and user trust. Provide rigorous written feedback, distinguish response quality levels, help define evaluation standards, and record short video explanations. The role requires senior-level software engineering experience, familiarity with AI-assisted development tools, strong TypeScript/JavaScript or Python skills, and excellent English communication.

Evaluates AI coding-agent interactions for technical quality, engineering judgment, reasoning, explanations, and user trust. Provides detailed written and video feedback, distinguishes response quality levels, and helps define standards for tools such as Codex, Claude Code, and Cursor. This flexible, remote contract requires senior-level software engineering experience, strong TypeScript/JavaScript or Python skills, familiarity with AI-assisted development, and excellent English communication.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and developer trust. Provide clear, opinionated feedback, distinguish response-quality levels, and help define standards for excellent interactions with tools such as OpenAI Codex, Claude Code, and Cursor. This contract role requires staff- or principal-level software engineering experience and familiarity with modern AI-assisted development workflows.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for engineering quality, reasoning, usefulness, and judgment. Provide rigorous written feedback, distinguish response quality levels, assess explanations without executing every line, and help define standards for effective AI-assisted development workflows. The role is remote, flexible, contract-based, and requires strong communication skills and senior-level software engineering experience.

Build and deploy production-grade agentic AI solutions across Palantir Foundry, Azure, and Databricks. Responsibilities include designing modular agent skills, extending enterprise ontologies, engineering data pipelines, integrating and optimizing LLMs, developing CI/CD pipelines, and delivering use cases from experimentation through production while collaborating directly with clients.

Develop and deploy production-grade AI and machine learning applications as part of a backend Python team. Responsibilities include integrating ML models and data pipelines, developing LLM prompts, supporting NLP, machine translation, and text-to-speech solutions, building CI/CD pipelines, implementing testing, and collaborating with Product and Engineering teams. The role requires strong Python and C++ skills, production deployment experience, and familiarity with ML frameworks and cloud-native tools.

Develop, integrate, secure, and deploy AI-enabled software solutions across cloud and on-premises environments. Translate mission needs into scalable technical architectures, conduct trade studies, harden end-to-end systems, and incorporate secure open-source tools. Lead cross-disciplinary engineering efforts, communicate technical decisions to leadership, deliver measurable mission value, and mentor engineers. The role is on-site in San Diego and requires an active Secret clearance and U.S. citizenship.

Designs, tests, and optimizes prompts for AI tools while creating and refining marketing and business content. Develops reusable prompt frameworks, ensures outputs match brand voice, fact-checks AI-generated content, and collaborates with marketing, sales, and operations teams. Continuously evaluates AI performance, stays current with prompt engineering techniques, and applies analytical judgment to improve content quality and effectiveness.

Architect and operate Cisco’s AI Compute Platform, developing distributed control-plane services, Kubernetes platforms, GPU schedulers, APIs, controllers, and automated remediation systems. The role manages multi-cluster environments, tenant quotas, workload routing, GPU allocation, and reliability for large-scale training and inference workloads. It also leads architecture reviews, establishes engineering standards, mentors engineers, and drives Infrastructure as Code, GitOps, CI/CD, observability, incident response, and operational excellence.

Build and operate Orpex’s agentic infrastructure, including production LLM agents, orchestration, memory, retrieval, human approval workflows, and evaluation systems. Own the full development loop from implementation and measurement through failure analysis, reliability improvements, and learning. The role ships to production immediately and may also involve infrastructure work, customer calls, and hiring.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Build and own Custral’s AI agent runtime and full-stack product surfaces. Develop tool gating, context assembly from Postgres and OpenSearch, model selection, tracing, evaluation harnesses, and regression suites for LLM systems. Ship React, Node.js, and Express functionality to real users, improve agent capabilities, and ensure model and prompt changes deliver measurable customer impact. Work independently in an early-stage, in-person New York City environment.

Designs and develops secure, performant Copilot products and platform services across Windows, Mac, and mobile. Builds distributed AI and agent-driven systems capable of reasoning, planning, and executing tasks. Collaborates with infrastructure, platform, application engineering, and research teams to ship high-quality software. Champions AI-assisted development, coding-agent adoption, prompt design, tooling best practices, and human-in-the-loop systems.

Optimize production AI workloads on Arm edge devices for performance and power efficiency. Develop kernel- and system-level implementations for DNN models using AI optimization tools and profiling techniques. Create production reference implementations and technical documentation, diagnose complex customer performance issues, and collaborate across engineering teams. Translate technical findings into recommendations for engineers and senior leadership while influencing Arm’s IP and software roadmaps.

Design, build, and operate autonomous AI agents that automate SRE, DevOps, and DBA workflows. Develop safe agent architectures with planning, tool use, error recovery, evaluation, auditability, and human escalation. Integrate agents with Kubernetes, Terraform, cloud APIs, databases, monitoring platforms, and CI/CD systems. Partner with infrastructure teams to identify automation opportunities and shape Airwallex’s infrastructure AI strategy.

Build and deploy AI systems for life sciences research institutions. Partner with scientific and engineering teams to integrate Claude into research workflows, develop MCP servers, benchmarks, agent skills, and domain-specific infrastructure, and identify deployment challenges involving heterogeneous data and auditability. Create documentation and technical content that enables partners to self-serve and scale successful solutions across the life sciences ecosystem.