Top AI & Machine Learning Jobs

Designs the technical foundation for Dave’s AI Agent Factory, including agent architecture, MCP servers, governed data access, semantic-layer integrations, evaluation frameworks, deployment standards, observability, authentication, authorization, auditability, and PII protection. The role establishes scalable AI engineering standards and reusable infrastructure while partnering with Data, Engineering, Security, and Product teams to enable safe, reliable company-wide AI adoption.

Architects, develops, deploys, and optimizes secure AI, machine learning, data, and platform capabilities. Builds predictive models, generative AI and LLM solutions, data pipelines, semantic models, evaluation frameworks, and production services. Establishes monitoring, testing, observability, governance, and lifecycle practices while partnering with business and technical teams to deliver measurable outcomes. Provides technical leadership, mentors staff, and advances complex initiatives with strategic autonomy.

Leads end-to-end data science initiatives, including data extraction, cleansing, feature engineering, exploratory analysis, machine learning model development, tuning, deployment, monitoring, visualization, and generative AI implementation. Translates complex business problems into actionable insights, develops advanced models, influences executive decisions, leads strategic projects, and mentors less experienced staff while collaborating across organizations.

Lead AT&T's causal measurement and experimentation practice, building scalable causal models and experimentation frameworks (MMM, causal inference, causal ML) to quantify incremental business impact, translate insights into decision-grade recommendations, and drive adoption across marketing. Own end-to-end measurement ecosystem, validate methods, influence executives, and develop a high-performing analytics team.

Build, deploy, support, and scale AI-enabled enterprise applications, automations, integrations, and internal tools. Translate business workflows into governed production solutions, establish reusable deployment and support patterns, and assist citizen developers. Collaborate across IT, manufacturing, engineering, quality, supply chain, finance, HR, and other functions on testing, security, documentation, adoption, monitoring, and handoff. Measure business value, troubleshoot applications, and guide responsible AI use.

Lead low-TRL research developing hybrid physics-informed and ML algorithms for advanced signal processing and PNT in contested electromagnetic environments. Evaluate academic research, prototype robust state-estimation, anomaly-detection and fusion methods, provide technical leadership as Project Design Authority, contribute to bids, and mentor junior engineers.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

Lead the design of APEX AI productivity benchmarks and expert-built datasets. Define task taxonomies, grading rubrics, contamination controls, statistical methods, and evaluation standards. Collaborate with academic, industry, engineering, product, data operations, and GTM teams to scale benchmarks, publish findings, and communicate results to technical and commercial audiences. Track LLM evaluation research and translate benchmark insights into company strategy and customer narratives.

Build, deploy, and operate applied AI systems connecting frontier research with data delivery. Develop scalable post-training and evaluation pipelines, synthetic data generation systems, data-quality infrastructure, benchmarks, and evaluation frameworks. Partner with AI labs and customers to understand requirements, prototype solutions, lead technical discussions, and deliver reliable production systems.

Build and scale agentic products, LLM agents, harnesses, verifiers, reinforcement learning environments, orchestration pipelines, and full-stack interfaces. Develop Python services and data infrastructure, instrument agent behavior, create evaluations, detect failure modes, and improve reliability, cost, latency, and user experience. Partner with research, product, design, and operations teams to define agent autonomy and human-in-the-loop workflows. The role is fully in-person five days weekly in San Francisco, New York City, or London.

Develops and evaluates statistical, machine learning, and AI models for internal business initiatives. Responsibilities include data cleaning, feature preparation, exploratory analysis, prompt engineering, LLM applications, hypothesis testing, time series analysis, validation, deployment support, documentation, and translating results into actionable insights. Collaborates with senior data scientists, analytics engineers, and business stakeholders. This is an in-office role requiring residence in the metro Phoenix area.

Lead the integration and production deployment of AI and machine learning capabilities across APIs, SDKs, automation, cloud engineering, MLOps, GenOps, and AIOps. Design, test, deploy, maintain, and observe scalable software with CI/CD automation. Coordinate multidisciplinary stakeholders, support technical documentation, and mentor early- and mid-career engineers while balancing tactical and strategic priorities.

Lead vision, roadmap, and delivery of enterprise data and AI products. Translate business goals into AI/analytics solutions, define KPIs, prioritize backlogs, collaborate with engineering, data science, and Agile Release Train teams, and iterate using usage metrics and VOC research to improve generative AI solutions.

Develops and trains machine learning models for semiconductor equipment and processes. Analyzes data for trends, anomalies, and improvement opportunities; builds predictive methodologies using statistical techniques; collaborates with cross-functional teams to deploy solutions; validates performance and drives continuous improvement for digital twin and operational decision-making initiatives.

Designs methods to consolidate and analyze structured and unstructured big data. Performs statistical and data-mining analysis, develops analytical models and algorithms, designs data acquisition and database structures, and cleanses and integrates large datasets using SQL, PHP, and Python. Partners with internal customers on requirements and reporting while supporting manufacturing and process-information products through advanced analytics.

Design and build enterprise-scale agentic AI platforms supporting multi-agent orchestration, memory, RAG, knowledge graphs, governance, observability, security, evaluation, and cost attribution. Develop scalable API-driven, cloud-native AI services with Azure technologies, event-driven architectures, messaging, monitoring, and responsible AI controls. Partner across business domains to establish agent onboarding, lifecycle management, operational controls, and reliable autonomous workflows.