Top AI Engineer Jobs

Lead public cloud platform engineering for AWS environments, guiding application teams through cloud adoption, production support, reliability improvements, incident resolution, automation, and performance optimization. Build resilient, secure platforms using AWS services, containers, Kubernetes, infrastructure-as-code, monitoring, and DevOps tools. Evaluate production readiness through resiliency testing and chaos engineering while collaborating cross-functionally and applying AI/LLM tools to diagnose infrastructure issues.

Design AI integrations for government systems, collaborate on SDKs and APIs, and ensure ethical AI deployment while maintaining high standards for code quality.

Design, build, and deploy AI integrations between frontier models and government systems. Develop SDKs, APIs, developer tools, and production-grade features; partner with stakeholders to prototype, iterate, and ensure safe, transparent, and ethical AI deployment for federal agencies.

Design and build AI-powered cybersecurity platforms that process large-scale telemetry, detect and predict threats, and generate explainable alerts. Develop scalable Python services, distributed data systems, AI DevOps pipelines, agentic workflows, RAG capabilities, and model integrations. Collaborate with security experts, product managers, and engineers to validate AI features and improve analyst workflows for security operations.

Design and build AI-powered cybersecurity platforms that ingest and normalize massive security telemetry, correlate events, predict threats, and generate explainable findings. Develop Python backend services, distributed storage systems, and AIDevOps pipelines for rapid experimentation. Integrate foundation models, agentic systems, RAG, embeddings, and graph-based models into analyst workflows. Collaborate with product teams, security experts, and engineers to deliver scalable, trusted threat detection and automation capabilities.

Connects frontier AI model APIs to evaluation harnesses, runs large-scale benchmarks, analyzes model performance and failure modes, and translates findings into client reports. Supports technical pre-sales with AI labs, manages internal data systems and catalogs, curates datasets, audits provenance, and owns precise last-mile data packaging and delivery. The role requires strong Python, API, SQL, model evaluation, data manipulation, and client communication skills.

Build and operate backend services, batch and real-time data pipelines, document-processing systems, APIs, integrations, and data models supporting an AI platform. Develop reliable OCR and LLM workloads, data quality monitoring, secure exports, cloud infrastructure, and CI/CD workflows. Design for distributed-system failures, authentication, authorization, and sensitive-data protection. Contribute to architecture decisions, maintain legacy services, support internal tools, mentor junior engineers, and improve engineering standards.

The role involves developing and enhancing a revenue cycle management platform, ensuring efficient data access for healthcare providers, and collaborating with clients to address their needs.

Designs, prototypes, and implements AI-enabled solutions that improve productivity, automation, and decision-making across engineering and business functions. Evaluates AI tools and platforms, integrates capabilities into existing workflows and development processes, and supports organizational adoption through guidance and demonstrations. Collaborates with engineering, security, compliance, and business teams to deliver reliable solutions for sensitive and classified environments while advising on AI trends, security, compliance, and adoption strategies.

Develops and integrates AI-enabled solutions that improve productivity, automation, and decision-making across engineering and business functions. The role researches and prototypes AI tools, supports secure deployment in classified environments, guides organizational adoption, and collaborates with engineering, security, compliance, and program teams. Responsibilities include evaluating emerging technologies, integrating AI into workflows and CI/CD processes, ensuring reliability and compliance, and mentoring junior engineers.

Develops and integrates AI-enabled solutions that improve productivity, automation, and decision-making across engineering and business functions. Responsibilities include researching AI tools, prototyping and deploying solutions, integrating capabilities into workflows and CI/CD processes, supporting adoption, and advising on emerging technologies. The role collaborates with engineering, security, compliance, and business teams while ensuring solutions meet security requirements for sensitive and classified environments.

Build AI tools, agent workflows, APIs, automation systems, and evaluation capabilities for robotic research and engineering. Automate model training, experiment management, simulation and real-robot testing, logging, analysis, debugging, and failure evaluation. Design statistically rigorous experiments, integrate emerging AI technologies, and deliver reliable, secure, human-approved systems. The role requires hands-on robotics experience, strong Python engineering, Linux, CI/CD, Docker, APIs, distributed systems, and collaboration with machine learning, simulation, and robotics teams.

Research and develop embodied AI systems, including VLA and world models, for physical robots. Responsibilities span multimodal model training, large-scale robotics data pipelines, distributed optimization, deployment, real-world testing, debugging, and performance improvement. The role integrates perception, control, long-horizon planning, robot-environment interaction, and foundation-model research across the full lifecycle from data collection through deployment.

Own customer-facing AI, robotics, and autonomous driving projects from discovery through delivery. Translate technical and data requirements into scalable solutions, define workflows and project plans, coordinate internal and external stakeholders, resolve risks, and ensure quality delivery. Support technical proposals, statements of work, pricing, account expansion, and product feedback while building long-term customer relationships.

Lead the architecture and long-term technical direction of Databricks AI Runtime, a serverless GPU training platform. Drive reliability, observability, developer experience, GPU efficiency, orchestration, scheduling, data loading, checkpointing, and multi-node training capabilities. Partner with research, product, customers, and engineering teams to resolve complex technical trade-offs, anticipate GPU and workload trends, mentor senior engineers, and deliver scalable, cost-effective AI infrastructure.

Build and productionize computer vision, 3D perception, and LLM-powered systems using proprietary LiDAR and imagery datasets. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and AI copilots while creating metrics tied to customer outcomes. Collaborate across hardware, software, and customer teams to rapidly prototype and deliver reliable infrastructure intelligence products.

Build, deploy, and improve AI agents for enterprise moving companies. Responsibilities include writing production code, designing AI workflows, integrating customer systems, debugging agent failures, optimizing prompts and evaluations, leading customer deployments, and translating recurring customer needs into scalable product features. The role combines software engineering, AI implementation, technical delivery, and direct collaboration with customer leadership.

Build enterprise data models, semantic layers, metadata, SQL transformations, and cloud data pipelines. Improve text-to-SQL and generative BI accuracy by partnering with AI engineers on RAG, agents, prompts, testing, and guardrails. Translate business needs into data requirements, validate KPIs, document schemas, apply quality and governance standards, and collaborate with technical and non-technical stakeholders across pharma, life sciences, manufacturing, and healthcare projects.

Build and productionize computer vision, 3D perception, and LLM-powered systems using LiDAR and imagery to assess infrastructure. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and customer-facing AI tools while defining performance metrics and evaluating emerging technologies.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Define Scribd’s technical vision for agentic engineering and establish architecture, patterns, and guardrails for AI-assisted software development. Lead internal developer tools, libraries, platforms, and engineering workflow improvements across codebases, CI/CD, and infrastructure. Partner with engineering leadership on ambiguous cross-organizational initiatives, mentor senior and staff engineers, and drive adoption of coding-agent capabilities throughout the organization.

Lead a small autonomous engineering team combining experienced developers with AI coding agents. Own architecture, technical direction, software implementation, delivery, quality, security, reliability, and production operations. Establish AI-assisted engineering workflows, testing and deployment guardrails, and scalable development practices. Mentor two engineers, translate business objectives into technical solutions, resolve blockers, and continuously improve engineering productivity and software quality through hands-on leadership.

Build, test, deploy, and support production software, including product features, APIs, integrations, and internal tools. Use AI coding agents such as Claude Code and OpenAI Codex to accelerate development while reviewing generated code for correctness, security, and performance. Write automated tests, debug issues, participate in code reviews, maintain documentation, and own features through production readiness. Collaborate with engineers, clarify requirements, and continuously improve software delivery practices.

Design and implement AI-enabled workflow orchestration, integrations, and automation pipelines across legal workflows. Build agent orchestration, vector retrieval, API/webhook integrations, monitor reliability and performance, ensure governance and compliance, and collaborate with product, legal, and operations teams to scale AI-native legal service delivery.

Design and implement AI-native workflow orchestration and automation systems for legal services. Build integrations between AI models, CRMs, document systems, client portals, and internal tools. Develop agent workflows, vector retrieval and observability pipelines, monitor reliability and performance, troubleshoot integrations, and support governance, compliance, and scalable client experiences.