Machine Learning Jobs in Tempe, AZ

Design, develop, deploy, and productionize AI/ML and generative AI solutions (including conversational AI). Collaborate with stakeholders, support production operations, participate in deployments, mentor teams, and drive end-to-end delivery using cloud platforms, MLOps, and enterprise tools.

Develop and optimize predictive models for credit and risk, leveraging advanced machine learning techniques to improve accuracy and decision quality.

Lead the Forward Deployed AI Engineering team to build solutions, engage customers, and enhance cybersecurity with AI tools and data science.

Lead and build an adversarial research team to track advanced threat actor campaigns, perform malware analysis and adversary emulation, and translate findings into high-fidelity, AI-driven detections. Publish research, represent the company publicly, and collaborate with SOC, IR, and engineering to operationalize intelligence into detection logic and response.

The Vice President of Data Science oversees AI-driven clinical insights and predictive modeling, leading a team to develop novel AI signatures from clinico-genomic data. Responsibilities include strategy execution, model governance, and collaborative efforts across multiple departments to enhance precision medicine.

Provide TCAD modeling and EDA tools support for device design and product engineering. Resolve support tickets, review vendor roadmaps, develop EDA enhancements including AI agents and workflow automation, and work across silicon and wide bandgap technologies.

Develop, deploy, and maintain quantitative and optimization models for BESS dispatch, renewables, gas generation, and power market analysis. Build CWENQuant platform, data pipelines, and dashboards; create stochastic and ML/DL forecasts; perform mark-to-market, underwriting, and risk analysis; benchmark models and provide analytical insights to trading, origination, development, and asset management.

Lead complex analytics projects supporting client experience transformation: gather and validate structured and unstructured data, apply advanced analytics and machine learning, produce executive visualizations and recommendations, manage recurring reporting, enforce data quality standards, and mentor other analysts to drive business decisions and improve client outcomes.

Lead end-to-end analytics for Flagship Client and Distribution: partner with stakeholders to frame problems, perform advanced analyses and machine learning, ensure data quality, develop recurring analytics products and executive visualizations, and mentor analysts to drive data-driven decisions.

Seeking a Senior Cybersecurity Analyst for monitoring threats, responding to incidents, and utilizing AI/machine learning tools within SOC. Responsibilities include threat detection, incident analysis, and integration of Google SecOps tools.

Design and implement Dynamics 365 Finance solutions, lead fit/gap and integration architecture, optimize finance processes (GL, AP, AR, cash, fixed assets, project accounting), oversee project delivery, enable reporting and AI-driven automation, mentor functional teams, and advise finance leadership on ERP, Copilot, and data/AI strategy.

Lead development and deployment of monitoring, modeling, and analytics tools to maximize solar and battery storage asset performance. Detect and analyze underperformance, implement corrective actions, support procurement and due diligence, coordinate testing and handovers, and train team members on PVsyst and performance methods.

Lead complex analytical projects end-to-end; design scalable dashboards, data models, KPI frameworks; perform advanced analyses to inform decisions; partner with Finance, Technology, and Data Engineering; improve processes and data quality; mentor junior analysts; communicate insights to business leaders and identify automation and efficiency opportunities.

Design, build, and operate large-scale AI training and inference infrastructure. Responsibilities include GPU/accelerator cluster management, scheduling, storage and networking (RDMA/InfiniBand/NCCL), integrating ML frameworks, observability, fault tolerance, cost optimization, developer tooling, security, automation, and operational documentation.

Design and ship applied ML systems from problem framing through deployment. Prototype, evaluate, and optimize models across NLP, vision, recommendation, and structured data. Build production training/inference pipelines, implement rigorous experiments, ensure observability and safety, and mentor engineers while influencing AI roadmap.

Optimize end-to-end ML training and inference for throughput, latency, and cost. Profile bottlenecks across data, compute, communication, and memory; implement model compression, parallelism, compiler- and kernel-level optimizations; build benchmarks and regression suites; collaborate with product and engineering to deploy production-ready performance improvements and document tuning playbooks.

Design and ship production-ready ML systems: prototype and evaluate models (NLP, vision, recommendation), build training/inference pipelines, optimize for performance and cost, implement rigorous experimentation and responsible-AI evaluations, and mentor engineers while influencing the AI roadmap.

Apply statistical and machine learning methods to workforce data to generate insights for talent, retention, and workforce planning. Build predictive models, dashboards, automated reporting, and support HCM implementations while ensuring data governance, privacy, and ethical use of employee data. Partner with HR, Finance, IT, and business leaders to translate analyses into actionable recommendations.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Lead AI/ML product management for strategic, cross-divisional products. Define features, implementation plans, and market analysis; drive CI/CD, automated deployment, and UX improvements. Coach technical teams, influence stakeholders, and align business outcomes to product strategy.

The Senior Machine Learning Scientist will develop LLM and CV models, mentor junior scientists, and advance AI technologies applicable to law enforcement.

The Senior ML Operations Engineer is responsible for building and maintaining ML infrastructure, model deployment processes, and collaboration with data teams to optimize predictive modeling solutions.