Top AI & Machine Learning Jobs

Lead the architecture, development, deployment, and monitoring of production-grade machine learning systems for high-definition, ADAS, ISA, and IVI maps. Lead work across computer vision, NLP, deep learning, Generative AI, Agentic AI, MLOps, data ingestion, model training, inference, and scalable cloud or in-vehicle deployment. Provide technical leadership, conduct architecture and code reviews, integrate ML outputs with mapping workflows, and translate business requirements into reliable ML system designs.

Lead design and implement agentic AI systems: integrate services and models, optimize and fine-tune LLMs (LoRA/QLoRA), build datasets and evaluations, run experiments to improve reliability, cost, and latency for production workflows.

Lead enterprise AI strategy and delivery, building and scaling LLM-powered applications, agentic systems, RAG/vector retrieval, orchestration, evaluation, governance, and production-grade AI platforms. Partner with engineering, data, cloud, security, and business stakeholders to deploy secure, observable, and scalable AI solutions and drive organizational adoption and transformation.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Design, build, and operate an enterprise AI Hub providing shared runtime, CI/CD, model registry, observability, governance, and developer tooling to enable scalable, secure production AI and self-service capabilities across cloud and hybrid environments.

Design, build, deploy, and optimize production-grade generative AI agents, copilots, and intelligent workflows. Lead end-to-end delivery from solution design and prompt engineering to deployment, monitoring, and continuous improvement. Partner with business stakeholders to transform processes for AI-first execution, define KPIs, ensure governance and security, provide training and change management, and contribute reusable frameworks and best practices across the AI Center of Excellence.

Lead strategy and technical direction for generative and multimodal vision systems. Build end-to-end model workflows, improve realism, controllability, and robustness, drive evaluation and production-readiness, partner with engineering to deploy optimized inference, and mentor scientists while owning a major technical pillar from research to production.

Design, build, and operate AI platform and orchestration systems for computer vision, perception, simulation, and mapping. Own pipelines, artifact handoffs, workflow control, deployment, observability, and APIs; enable reproducibility, tracking, and scalable production inference while partnering with data scientists to reduce iteration friction.

Lead architecture and delivery of perception and spatial AI models from cloud training to deployment on automotive-grade SoCs. Define deployment-first architectures, convert state-of-the-art research into production pipelines, set evaluation and hardware-aware validation standards, partner across research/product/customers, remain hands-on prototyping and debugging, and hire and mentor a high-performing engineering team.