Top AI & Machine Learning Jobs

Leads the data science function, setting strategy, technical direction, governance, and machine learning standards. Partners with senior leaders to prioritize analytics initiatives across acquisition, retention, customer experience, pricing, and portfolio performance. Personally develops and validates advanced models while leading production deployment, monitoring, and continuous improvement. Manages, mentors, and grows a team of data scientists and machine learning engineers, ensuring reliable, explainable, and business-impactful decision systems.

Lead the architecture, development, deployment, and scaling of AI-native solutions for federal clients. Build full-stack applications, RAG systems, semantic search, agentic workflows, and reliable data pipelines across structured and unstructured data. Guide embedded engineering teams, integrate solutions into legacy and classified environments, evaluate model performance, uphold responsible AI practices, and serve as the technical lead in client engagements. Travel may reach 100% based on mission needs.

Develop and evaluate AI/ML-driven automation for ASIC and VLSI design and implementation. Explore GenAI, reinforcement learning, graph neural networks, and convolutional networks to optimize power, performance, area, and quality. Build prototypes, pilot automation on real SoC and IP designs, and collaborate with architecture, design, verification, implementation, academic, and industry teams. Contribute to CAD methodologies, tools, and digital transformation initiatives for advanced automotive processors and microcontrollers.

The Senior Data Science Engineer will lead the development of machine learning projects, build scalable data pipelines, and collaborate with teams to implement solutions while ensuring quality and business impact.

Supports federal data and AI engagements by preparing data, developing and validating machine learning and NLP models, building ETL/ELT processes, and producing analytics, dashboards, documentation, testing, and client deliverables. Collaborates with senior staff and clients to define problems, discover data sources, assess risks, and deliver analytical solutions. The role requires production-level coding, distributed computing and cloud platform experience, and an active Secret clearance. Hybrid work is based near Ft. Belvoir, Virginia, with travel as required.

Leads federal data and AI engagements from planning through delivery, including client workshops, data discovery, ETL/ELT, annotation, machine learning model development, validation, deployment, and reporting. Oversees complex workstreams, budgets, risks, deliverables, dashboards, and stakeholder expectations. Develops production-level AI software using distributed computing, big data platforms, cloud infrastructure, NLP, information retrieval, and machine learning on unstructured data.

Develop and implement data science and generative AI solutions across the full machine learning lifecycle, including data engineering, model development, evaluation, and operational rollout. Apply RAG, prompt engineering, few-shot techniques, deep learning, NLP, and machine learning to business problems. Collaborate with product managers, business leaders, and engineers while ensuring ethical and regulatory alignment. This in-person early-career program includes structured learning, mentorship, networking, and exposure to AIG’s insurance business.

Build and deploy predictive models for workforce attrition, turnover, hiring, and headcount forecasting. Develop AI agents that query and act on sensitive People data, using LLMs, tool orchestration, retrieval, and guardrails. Apply experimentation, causal inference, and uncertainty quantification; extract and structure complex HR data with SQL and APIs; monitor model performance; partner with HR and Data Engineering; and explain findings to nontechnical leaders.

Work with strategic clients to design and implement scalable AI integrations and production-ready workflows, build APIs and data pipelines, debug client environments, lead technical discovery, and drive MLOps and human-in-the-loop deployments to transition AI from proof-of-concept to reliable production.

Build and maintain software that integrates ML/GenAI models with Kubernetes-based edge orchestration. Develop controllers, operators, CRDs, and Helm charts to schedule and deploy AI workloads at the edge, integrate ONNX/PyTorch/TensorFlow models, use AI coding agents, contribute to open-source components, and collaborate across AI, infrastructure, and product teams to ship features end-to-end.

Summer intern supporting enterprise AI adoption and workplace productivity. Responsibilities include identifying workflow improvement opportunities, testing and documenting Microsoft Copilot and Microsoft 365 workflows, creating employee training materials, researching responsible AI and prompt engineering practices, gathering user feedback, and recommending future AI pilots. The role emphasizes business process improvement, communication, analytical problem-solving, and practical use of approved AI tools rather than software development.