Top Remote Machine Learning Jobs in Tucson, AZ

Serve as second-in-command for Coinbase Derivatives' Market Regulation Department, leading day-to-day regulatory compliance, managing exams and audits, advising on CFTC Core Principles, reviewing surveillance escalations and enforcement actions, and coordinating cross-functional regulatory workstreams to ensure the DCM meets obligations and resolves findings timely.

Lead planning, deployment, tuning, and operational support of CrowdStrike Falcon's AI/ML detection and response capabilities. Implement and optimize ML models, develop automation playbooks and Falcon API integrations, troubleshoot AI-related issues, support incident response and threat hunting, and deliver customer workshops and collateral. Up to 25% travel.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Use advanced analytics and ML to design and monitor credit risk models, optimize underwriting and credit strategies, partner with Engineering/Product/Compliance, oversee portfolio performance and regulatory alignment, and lead cross-functional initiatives to balance growth, compliance, and risk mitigation.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

The Senior Product Manager will oversee the strategy and development of telematics data products, manage cross-functional teams, and ensure product viability and market alignment through customer insights and technical fluency.

The Director of Product Management for ML Platform will own the product strategy and execution, mentor PMs, and collaborate with ML teams to enhance infrastructure and manage costs.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Lead planning, deployment, tuning, and operational support of CrowdStrike Falcon's AI/ML detection and response capabilities. Implement and troubleshoot AI-driven detection models, automation playbooks, API integrations, and threat-hunting for customers. Provide training, mentorship, and customer-facing collateral while optimizing detection efficacy and integrating Falcon into customers' security workflows.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

As an AI Engineering Manager at PwC, you will lead the design and operation of AI-powered platforms, mentor engineers, and ensure project delivery excellence while focusing on security and scalability.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

Build and operate ML training and serving infrastructure; design, develop, and launch backend systems; collaborate across teams to decompose projects; maintain availability through monitoring and on-call support; review and debug large codebases; contribute to hiring and team growth.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.