Top AI & Machine Learning Jobs

Own strategy and delivery of AI-driven products across customer and internal use cases. Lead discovery, write requirements, partner with engineers, design experiments/A-B tests, monitor performance, and champion responsible AI practices.

Lead cross-layer efforts to improve large language model efficiency across architectures, training and inference systems, GPUs, memory, interconnects, and distributed infrastructure. Establish an efficiency roadmap, identify bottlenecks, and guide optimization projects from research through production. Partner with model researchers, systems engineers, compiler and kernel developers, and hardware architects to shape model, software, accelerator, and datacenter roadmaps. Apply performance analysis, benchmarking, workload characterization, and hardware-aware optimization to improve throughput, cost, memory, latency, power, and training time.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

Develop descriptive, predictive, and causal analytics for local government datasets. Responsibilities include maintaining data pipelines, cleaning and refreshing databases, weighting surveys, building statistical and machine learning models, conducting impact studies, automating cloud workflows, presenting insights, and documenting reproducible processes. The role partners with engineering, marketing, sales, customer success, and survey research teams, and requires Python, SQL, statistics, communication, and applied data science experience.

Analyze and clean data, develop statistical and predictive models, conduct A/B testing, identify business opportunities, and communicate insights to stakeholders. The role also involves evaluating data sources, creating tailored algorithms, implementing analytics, and monitoring model performance and data accuracy.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.

Leads a 20+ person organization of data scientists, machine learning engineers, and digital architects building and deploying predictive and diagnostic analytics for heavy-duty fleets. Owns the Analytics Foundry technical roadmap, model lifecycle, governance, standardization, and enterprise-scale productionization. Partners with reliability, product service, and insights teams to prioritize failure-mode analytics and operationalize digital products. Builds technical talent, improves model quality and deployment speed, applies Lean methods, and translates analytics into reduced downtime and increased customer service value.

Design, build, maintain, and document AI-powered workflows for creative teams. Translate creative needs into scalable systems, prototype and evaluate AI tools, optimize prompts and outputs, perform creative QA, assess vendors and models, and support production handoffs. The role bridges creative, production, strategy, engineering, and leadership while identifying practical opportunities for automation and communicating technical concepts clearly.

Supports business decision-making through data analysis, predictive modeling, statistical and machine learning techniques, and presentation of insights. Interns will work across marketing, lending, indirect lending, and warranties while gaining hands-on experience throughout the data science lifecycle. The role is an 11-week, paid, on-site internship based in Tempe, Arizona, or Dallas, Texas, with no relocation assistance.

Leads a data science team delivering personalized digital experiences through machine learning, generative AI, recommendation systems, predictive modeling, and responsible AI practices. Owns the AI lifecycle from experimentation and model development through deployment, monitoring, governance, and optimization. Partners with product, engineering, design, executives, and business leaders to identify opportunities, shape AI strategy, and integrate scalable solutions into customer-facing platforms.