Top AI Engineer Jobs

Design and implement scalable cloud solutions for autonomous vehicles, lead projects from inception to completion, and mentor junior engineers.

As a Staff AI/ML Full-Stack Engineer, you will design and build end-to-end software products, overseeing both frontend and backend development, while mentoring junior engineers and leading complex technical projects.

As a Senior Mobile Engineer at SoFi, you'll develop and optimize mobile app features, mentor teams, and lead technical direction for mobile systems.

Lead the technical strategy for developer tools in a financial services company, focusing on AI-enabled development practices. Responsibilities include architectural design, mentorship, and driving innovation in software development.

Join our Subscription Cancellation team as a Backend Software Engineer, designing and developing scalable microservices using Kotlin or Java and Spring Boot. Collaborate with a cross-functional team to maintain high engineering standards, troubleshoot production issues, and assist in deployment using CI/CD tools.

As a Senior Software Engineer II, you'll design and operate a Kubernetes-native platform for AI workload orchestration, improve performance, and mentor engineers.

Lead technical vision and execution for enterprise-scale AI, automation, and data initiatives. Architect scalable, secure AI systems and data pipelines, mentor engineers, drive cross-functional alignment, and deliver high-impact production solutions that improve productivity and business outcomes.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

As a Sr. Solution Architect, you'll lead Cloudflare's AI/Developer Platform sales, design solutions, and advise clients on product capabilities.

The AI Engineer will design intelligent agents for game development, improve workflows through AI, and collaborate across teams to create innovative game experiences.

The role focuses on connecting engineering teams within GTE to enhance AI integration, communication, and strategy, ensuring knowledge sharing and minimizing siloed efforts.

Design, develop, validate, and deploy end-to-end AI/ML solutions for aerospace systems. Lead data collection/preprocessing, model training and optimization, safety risk management, production integration, monitoring, and cross-functional collaboration while ensuring ethical, regulatory, and performance requirements.

As an AI Automation Engineer, you'll identify and build AI-driven automation systems for internal operations, rapidly prototype solutions, and enhance business workflow efficiency, ensuring systems are robust and secure.

Build and enhance AI applications for enterprises, focusing on frontend development using modern technologies like Next.js and React. Collaborate with teams to create intuitive user experiences and optimize UI performance.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

Lead end-to-end AI/ML projects: research, prototype, optimize, and productionize ML and LLM solutions for healthcare. Mentor and recruit teams, run experiments and A/B tests, present results to leadership, and pursue AI research and IP.

Lead design, development, deployment, and operationalization of AI/ML solutions (including LLMs and Generative AI) for healthcare. Provide technical leadership on chatbots, conversational AI, and NLP, ensure responsible AI and production reliability, and mentor engineering teams.

Design, develop, test, and deploy generative AI/ML models (LLMs, RAG, agentic AI) into production using agent frameworks, containerization, CI/CD, and cloud platforms; analyze large datasets and optimize system performance.

Design, develop, and productionize NLP/ML/AI solutions (LLMs, NLU/NLG/SLU) for high-traffic consumer systems. Lead end-to-end model lifecycle: data ingestion, feature engineering, modeling, deployment, optimization, monitoring, and POCs. Establish best practices, prioritize work, and collaborate on scalable, voice-based bot platforms built on LLM frameworks.

Lead AI/ML Engineer to design, develop, and deploy AI-powered solutions across the full ML lifecycle. Own architecture, MLOps, monitoring, and scalability. Partner with product, data science, and platform teams, mentor junior engineers, and drive adoption of best practices for production ML and generative AI solutions.

Develop and deploy LLM-based solutions using LangChain, fine-tuning, and RAG. Write production-quality Python code (PEP8), use Docker and Git, apply traditional ML (XGBoost, K-means), and ensure testing and documentation.

Lead design, development, deployment, and maintenance of AI-powered solutions (LLMs, Generative AI, traditional ML) for provider-facing products. Build chatbots and conversational NLP, integrate AI into cloud (preferably AWS), ensure responsible AI practices and compliance, provide technical leadership, mentor engineers, and collaborate with product and architecture teams.

Lead development of AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, ensure responsible AI compliance, produce documentation/patents/publications, and collaborate on MLOps pipelines and stakeholder integrations.

Lead end-to-end generative AI and ML initiatives: design scalable AI architectures, build and productionize models, run experiments and A/B tests, analyze healthcare datasets, implement MLOps, research novel approaches, and mentor/hire AI/ML engineers.

Design, develop, and deploy production-grade AI/ML models and pipelines (LLMs, RAG, Transformers). Work with agentic frameworks, scale training/inference on Databricks/PySpark, integrate solutions via Docker and REST APIs, leverage cloud services (Azure/AWS), participate in AI governance, and mentor junior engineers to deliver measurable pharmacy optimization outcomes.