Top Machine Learning Jobs in Tucson, AZ

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Responsible for designing, building, and operating AI-powered features, scalable APIs, and maintaining integrations within ClickHouse's AI/ML ecosystem.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

Own and improve recommendation and search systems across feed, discovery, and search. Build retrieval and ranking (candidate generation, embeddings, two-tower models), run experiments, create user/content/session representations, and ship production ML systems with monitoring to improve retention, engagement, and content distribution.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.