Top AI Engineer Jobs

Design, build, and deploy production ML systems for demand forecasting, labor optimization, and LLM-powered workflows. Own end-to-end ML lifecycle: data ingestion, feature engineering, model training, deployment, monitoring, drift detection, evaluation, and experimentation. Collaborate with Product, Engineering, and Operations to integrate models into core workflows and translate performance into business insights.

Lead design and development of integrated AI systems combining hardware, software, firmware, board, and silicon. Define system architectures, optimize performance, implement and benchmark solutions, and translate business opportunities into product specifications. Innovate in reinforcement learning, vision, simulation, and robotics while mentoring technical leaders and influencing Intel's AI product roadmap.

Partner with corporate functions to discover AI automation opportunities, design end-to-end agentic AI solutions, build production-quality Python services with CI/CD, monitoring and guardrails, pilot and drive adoption, ensure responsible data handling, and create reusable components and documentation for enterprise AI workflows.

Lead Responsible AI projects to detect, mitigate, and evaluate bias across GenAI, foundation models, and recommender systems. Collaborate with product and platform teams, deploy fairness interventions at scale, mentor junior engineers, and define technical strategy to align models with Pinterest policies and business metrics.

Lead technical design and execution for an AI/ML mission platform: drive architecture decisions, lead major engineering epics, simplify distributed systems, optimize edge and cloud performance, mentor engineers, integrate ML models and inference pipelines, and represent technical capabilities to DoD stakeholders.

Design, build, and deploy production-grade AI/ML and LLM-based systems. Lead end-to-end ML lifecycle, implement RAG and embedding pipelines, establish MLOps best practices, mentor engineers, and collaborate with cross-functional teams to deliver scalable, responsible AI solutions.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

Develop, analyze, and implement application systems using Python and generative AI frameworks. Build and maintain RESTful APIs, cloud-deployed containerized services, and ensure security, testing, and operational support. Consult with stakeholders, perform feasibility and post-implementation analysis, mentor junior analysts, and drive compliance with policies and risk controls.

Lead development of large-scale, multi-task robotic foundation models and manage the data flywheel: specification, collection, training, and real-world evaluation. Implement model specialization and fine-tuning, write production-quality Python for deep learning, and collaborate with robotics and hardware teams to deploy on real robots.

Develop, test, deploy, and integrate machine learning solutions across the ML lifecycle. Build data pipelines, APIs, scheduling and production support for ML/GenAI applications, refactor data scientist code into maintainable IT solutions, and configure AI/ML infrastructure across cloud and on-prem environments.

Join Knoetic as a Fullstack Product Engineer in a new AI pod. Own features end-to-end, work with customers, and improve engineering practices.

Design and build high-performance UI and APIs, manage database architecture, optimize systems for reliability and performance, and collaborate cross-functionally in a scientific context.

The AI Systems Engineer will design AI architectures, build training pipelines, optimize models for edge inference, and manage model lifecycles.

Lead the development of real-time, safety-critical onboard software for embedded GPU platforms, focusing on AI perception and optimization.

Contribute to AI-driven healthcare solutions by designing and building AI-centric features, integrating LLMs, and ensuring system reliability. Collaborate effectively and take ownership of projects in a fast-paced environment.

As a Senior Software Engineer, you will design and build AI-driven automation solutions, integrating them into backend workflows while ensuring system reliability and collaborating cross-functionally.

As a Senior Software Engineer, design and develop automation solutions leveraging AI, integrate AI-driven components, and maintain backend systems while collaborating cross-functionally to improve workflows.

Develop, maintain, and run automated and manual tests for AI-driven features and integrations. Collaborate with product, development, and data science teams to validate RESTful services, troubleshoot production issues, and produce QA documentation and test artifacts in an Agile environment.

Intern will develop AI/ML applications for containerized production environments, perform model selection and training, prototype software architectures, research technologies, and support customers by debugging and collaborating with global ACS teams.

Lead signal integrity analysis for photonic-electronic systems, collaborating with design teams to optimize high-speed data paths and package stack-ups.

The role involves applying AI and ML to solve real-world physics problems, building simulations, managing customer engagements, and translating data into actionable insights, all while maintaining high scientific rigor.

Lead the transition from teleoperation to autonomy in robots by developing Vision-Language-Action models and deploying ML systems. Collaborate on data processing and model integration for robotics applications.

The role involves enhancing kernel performance and AI-assisted tooling through developer workflows, observability infrastructure, and optimization techniques, collaborating across engineering teams.

Build and own autonomous, customer‑facing and internal agentic systems that drive revenue and pipeline. Execute end-to-end product development from prototypes to production, working across APIs, orchestration, data flows, frontend experiences, and deployment while partnering with marketing and sales stakeholders to measure impact.

As a Full Stack Engineer, you'll develop platform capabilities, collaborate with teams to implement solutions, and integrate new technologies while ensuring architecture and security are sound.