Top Senior Machine Learning Jobs in Tucson, AZ

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

Lead and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

Lead and scale Vibrant's data science, ML, and AI function: set strategy and governance, oversee applied research and production ML (especially NLP), ensure HIPAA-compliant deployments, partner with execs and engineering, recruit and mentor teams, and deliver measurable outcomes for crisis services and enterprise initiatives.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.

Design and lead end-to-end applied AI architectures (predictive ML, MLOps, generative AI, LLMs, RAG, agentic workflows), translate business needs into production-ready solutions, prototype and deliver implementations, and ensure security, observability, governance, and operational reliability.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

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.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.