Top AI & Machine Learning Jobs

Designs and builds secure enterprise AI architecture, integrations, agents, retrieval pipelines, and workflows. Owns AI technical delivery, evaluates platforms, establishes reusable architecture standards, and integrates AI with enterprise systems. Ensures deployments protect CUI and comply with NIST SP 800-171, CMMC, DFARS, and organizational security requirements. Partners with governance, security, and compliance teams while monitoring solution performance, cost, and reliability.

Lead NPU subsystem architecture for next-generation SoCs, including architectural exploration, microarchitecture, performance and power modeling, workload analysis, and hardware-software co-design. Develop cycle-accurate models, optimize CNN and LLM workloads, map AI use cases to NPU capabilities, and deliver architecture specifications. Collaborate with hardware, software, driver, OS, and system teams to implement high-performance, low-power solutions.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Develop high-performance, large-scale machine learning services for a programmatic advertising platform. Build and deploy AI models and data-driven algorithms for real-time ad scoring, ranking, and bid optimization. Work with data scientists and cross-functional teams on scalable data analysis, model development, production rollouts, troubleshooting, and continuous optimization using agile methods.

Build and deploy machine learning, predictive marketing, forecasting, and experimentation models. Develop scalable data pipelines, integrate diverse data sources, maintain data integrity, and deliver executive reporting insights. Analyze customer and market trends, support marketing measurement and product positioning, and translate complex technical findings into strategic recommendations. The role also involves mentoring junior staff and collaborating with IT to deploy analytical solutions.

Design and implement traditional machine learning and generative AI capabilities for electronic warfare products. Conduct research and development on emerging AI trends, support product design, ensure quality assurance, and transition capabilities into production. The role requires clearance, programming ability, and education or equivalent experience in machine learning or artificial intelligence. Prior intelligence community or Department of War experience is preferred.

Leads the development and deployment of advanced analytics, predictive models, AI and machine learning solutions. Builds data pipelines, prepares analytical datasets, monitors model performance, and operationalizes production-ready capabilities. Partners with business and technical teams to solve complex problems, communicate insights, establish governance practices, and deliver measurable outcomes. Provides technical leadership and mentorship while applying techniques such as forecasting, classification, clustering, NLP, generative AI, and LLMs.

Design, train, and deploy reinforcement learning agents that optimize power and compute resources across physical data centers. Develop multi-agent, model-based, and deep RL systems; build simulations; transition models into stable production deployments; improve strategies for peak shaving, workload shifting, and thermal management; and collaborate with platform engineers on APIs, telemetry, and infrastructure.

Leads the development and deployment of advanced analytics, AI, and machine learning solutions. Responsibilities include data preparation, pipeline development, predictive modeling, experimentation, model monitoring, generative AI applications, and production operationalization. Partners with business and technical teams, leads medium- to large-scale initiatives, mentors junior data scientists, communicates insights, promotes responsible AI, and delivers measurable business outcomes.

Build AI-powered workflows, features, integrations, and evaluations for insurance customers. Work across customer discovery, product, and engineering to solve operational problems, deploy solutions, investigate agent behavior, improve workflow reliability, and turn deployment patterns into reusable capabilities.