Top AI Engineer Jobs

The Principal Engineer will architect and develop scalable AI-driven data platforms, mentoring engineers and aligning technical strategies with business goals.

Support customer network operations with systems engineering and project management by analyzing AI/ML proposals, coordinating activities, and improving processes.

The Staff Software Engineer will develop and maintain infrastructure for AI research, improve tooling for researchers, and mentor engineers in creating scalable systems for experiments and model training.

As a Senior AI Platform Engineer, you will architect AI-driven systems, develop CI/CD pipelines, and optimize infrastructure for AI workflows.

Lead the design and development of machine learning solutions, overseeing the entire software development lifecycle and mentoring engineers. Drive innovation in Generative AI and high-performance systems.

The role involves designing and developing AI-driven software to improve geospatial data quality. Responsibilities include developing ML components, collaborating with cross-functional teams, and integrating AI algorithms for product releases.

Develop and maintain C++ machine control and data analysis software for advanced mask inspection systems. Collaborate with multidisciplinary teams to define system requirements, optimize performance, support integration and testing, and resolve customer escalations. Work in Linux environments and leverage RPC/REST APIs, containerization, and modern UI frameworks as needed.

The Senior Engineer, Enterprise AI designs and develops AI applications that enhance productivity and decision-making across T-Mobile's enterprise, utilizing various AI frameworks and collaborating with cross-functional teams.

Lead design, development, and production deployment of enterprise AI agents and LLM-powered tooling to automate engineering workflows. Define technical vision, set standards, mentor engineers, partner with stakeholders, and ensure reliability, observability, and scalability of agentic systems.

Develop and maintain AI tooling systems for space stations, focusing on frontend customizations, AI integration, and full-stack feature implementation.

Operate and support production AI infrastructure across global datacenters, focusing on high-performance NVIDIA GPU platforms, Kubernetes, and high-speed networking. Monitor and troubleshoot infrastructure, network, hardware, and platform incidents; participate in incident response and root cause analysis; improve observability, automation, and operational runbooks.

Design, build, and productionize end-to-end AI and generative systems that convert unstructured content into structured knowledge. Develop retrieval, semantic search, and knowledge-driven pipelines; ensure evaluation, reliability, explainability, and ongoing monitoring. Collaborate cross-functionally to align AI solutions with business priorities and optimize for latency, cost, and throughput.

As an AI/ML Engineer, you'll implement machine learning techniques, build AI models, and support national security applications, collaborating with various teams.

Design, build, and deploy production-grade AI/ML systems (NLP/LLMs, computer vision, generative AI). Create scalable data pipelines, microservices/APIs, and cloud- or edge-deployed models. Ensure security, observability, and performance; perform model optimization, versioning, A/B testing, and documentation. Collaborate with engineers, mission SMEs, and product teams to meet operational requirements.

Develop and prototype AI-driven applications, validate ideas with real data, and transition projects into production while collaborating with teams and customers.

Design and deliver derivatives hedging and financing solutions by building pricing, risk, and scenario models; develop data pipelines and analytics platforms; create AI-driven agentic workflows and knowledge tools; integrate AI with internal systems for real-time analytics; support front-office OTC derivatives, client pitches, and proprietary platform development.

Develop algorithms in machine learning and statistics to address complex challenges. Design data pipelines and deploy solutions using cloud infrastructure.

The role involves owning product areas, working on backend, frontend, and with LLMs/ML models, delivering high-quality experiments, and engaging customers.

The AI Platform Production Engineer will automate deployment and management of AI workloads, ensure system reliability, and enhance tooling for production services, leveraging extensive engineering expertise.

Manage deployment, versioning, and lifecycle of AI models across multi-cloud platforms. Define reusable AI patterns (RAG, orchestration, prompts, vectors), build embedding and retrieval pipelines, establish CI/CD and IaC, ensure telemetry and governance, collaborate with security and vendors, and provide platform documentation, training, and technical leadership.

Manage and optimize cloud-based streaming platforms, ensuring high availability and performance for real-time analytics. Collaborate across teams to enhance data pipelines and integrate AI solutions.

The role involves leading AI-augmented development practices, building AI/ML cybersecurity solutions, mentoring team members, and collaborating cross-functionally to enhance software delivery using advanced technologies.

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.

The role involves building AI platform capabilities, mentoring engineers, collaborating with teams, and scaling systems for large datasets as part of a legal tech transformation.