Top AI & Machine Learning Jobs

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.

Develop production software using AI-first engineering practices. Translate requirements into prompts, implementation plans, and acceptance criteria; generate and validate AI-assisted code; identify hallucinations, security issues, and logic gaps; create automated tests; and ensure architecture, performance, documentation, and production readiness. Build reusable prompt workflows, collaborate with product and technical teams, document decisions, and guide engineers in adopting AI-native development practices.

Designs and leads end-to-end AI/ML architectures for clients across industries. Advises stakeholders on AI strategy, leads discovery workshops, oversees production delivery, and establishes model lifecycle and AgenticOps practices. The role also supports presales, develops reusable AI offerings, and defines responsible AI governance, architecture standards, and best practices. Collaboration with data scientists, engineers, and developers is central to delivering scalable, secure, cloud-based solutions.

Develop data science systems for motion imagery and full-motion video programs. Analyze aircraft operations and perform multi-object tracking on live video. Work with operational, archived, and live data in Elastic, Oracle, and Kafka; develop Python-based solutions and deploy code to Kubernetes using current GPU technologies. Subject matter expertise is welcomed, while the role supports mission-focused government capabilities.

Build and deploy spatial AI capabilities for generative home floor plans. Responsibilities include training and adapting ML models, creating APIs and evaluation pipelines, improving reproducibility, managing latency and cost, extending research into product features, partnering on MLOps and deployment, and mentoring engineers and researchers.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Build and deploy AI and machine learning solutions for clients, moving rapidly from prototypes to scalable production systems. Responsibilities include generative AI, AI agents, retrieval-augmented generation, model evaluation, enterprise integration, software engineering, cloud deployment, testing, monitoring, and communicating technical recommendations to business and technical stakeholders.

Supports enterprise AI governance through system intake reviews, risk and control analysis, scorecard maintenance, framework research, inventory management, reporting, AI literacy, stakeholder workshops, documentation, and governance workflow improvements. The intern gains experience with responsible AI, NIST AI Risk Management Framework concepts, ISO/IEC 42001, Microsoft Copilot, AI adoption, and governance operations.

Analyze multimodal healthcare, genomic, clinical, and biomedical imaging data to support drug discovery, biomarker development, disease progression modeling, clinical trial design, and real-world evidence analyses. The role involves building statistical and machine learning models, developing data pipeline modules and interactive tools, collaborating with clinical, genomics, and biology teams, contributing to data strategy, and communicating findings through publications and stakeholder presentations.

Provides strategic and technical leadership for enterprise AI, machine learning, generative AI, and advanced analytics. Establishes AI roadmaps, standards, governance, and responsible AI practices; oversees model, agent, platform, and data solution development; and leads MLOps, infrastructure, optimization, and lifecycle management. Partners with executives and business leaders to deliver measurable outcomes, manages budgets and vendors, and builds high-performing AI/ML and analytics teams.