Top Remote Machine Learning Jobs in Tucson, AZ

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

Technical lead responsible for architecting, developing, deploying, and operationalizing AI/ML solutions (including RAG and agentic AI) in secure, regulated environments. Oversee model lifecycle, MLOps, validation, explainability, bias mitigation, and compliance with CMS cybersecurity/data governance. Advise stakeholders, support production transitions, produce ATO/configuration/incident documentation, and mentor junior engineers.

Support integration of open-source biological ML models onto GPU-based infrastructure, containerize and deploy models, optimize performance (distributed training, data loading, GPU utilization), collaborate with researchers, and produce documentation and APIs to enable biotech researchers to use models effectively.

The Applied Scientist AI/ML Intern will work on building data pipelines, finetuning LLMs, and deploying AI models in a collaborative team environment.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Design and implement production ML features, close behavior gaps with common frameworks, investigate divergences, improve performance and stability, and document designs and tests for geospatial and large-scale ML workflows.

Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.

As a Staff Machine Learning Research Scientist, you will lead ML research, develop evaluation frameworks, mentor researchers, and collaborate on clinical AI solutions.

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.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

Own end-to-end production ML systems for search, recommendations, and ranking: architecture, training pipelines, inference, deployment, monitoring, drift detection, and retraining. Partner with data, product, and backend teams to ship reliable, low-latency ML-driven product experiences and set standards for ML infrastructure and model health.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead and grow a team of ML engineers building production ML systems for Fetch's Ad Platform. Translate product strategy into roadmaps, guide design and implementation of models for ranking, targeting, bidding and forecasting, set metrics and experimentation, enforce architectural and operational standards, manage trade-offs across performance and reliability, and drive cross-functional alignment to improve advertiser and member outcomes.

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.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

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

Build and maintain internal web tools supporting ML workflows. Develop backend services, frontend features, and deployments; participate in on-call rotations and code reviews; collaborate with partner teams to scale ML tooling and improve authentication and deployment processes.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

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 the strategy and implementation of AI solutions in R&D, driving adoption and improving operational efficiency through advanced analytics. Collaborate with cross-functional teams to design AI/ML models and evaluate new technologies.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

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