Top AI & Machine Learning Jobs

Build production AI agent systems for healthcare workflows such as scheduling and medication refills. Develop orchestration, tool-use, conversation-state, recovery, evaluation, and ML pipelines. Analyze production conversations, identify failures, create evaluation datasets, measure agent quality, and improve reliability across models and prompts. Partner with research to productionize techniques while balancing quality, latency, and cost.

Shape and improve DTCC’s enterprise AI governance framework. Review and challenge AI use cases, manage lifecycle governance, maintain AI inventories and attestations, develop risk metrics and reporting, support audit and regulatory readiness, and advise product, engineering, risk, compliance, legal, and data stakeholders on responsible AI controls and standards.

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Design and execute product analyses using raw user behavior data, own A/B experiments from design through interpretation, and translate findings into recommendations for product and engineering teams. Build repeatable analytical tooling, apply statistical modeling, and use AI tools to accelerate exploration, analysis, and insight delivery across consumer products, media, gaming, and e-commerce.

Develops AI-enabled applications for federal cybersecurity audit readiness, compliance analysis, governance, and risk management. Builds GPT, RAG, agentic workflow, vector database, data pipeline, dashboard, and predictive analytics solutions using audit and compliance data. Implements secure AI governance, model evaluation, prompt security, logging, explainability, and NIST-aligned controls. Collaborates with senior government stakeholders to translate cybersecurity requirements into proof-of-concept and production capabilities.

Train and evaluate learned manipulation policies for autonomous excavators using real fleet data and simulated rollouts. Explore diffusion and flow-matching architectures, conditioning, and hyperparameters; build evaluation pipelines for task success, smoothness, safety, and operator-likeness; investigate failure modes and sim-to-real issues; and contribute reproducible code while collaborating with behavior, controls, simulation, and autonomy teams.

Develop and deploy traditional machine learning, GenAI, and NLP solutions for healthcare, insurance, and life sciences applications. Responsibilities include model research, validation, documentation, deployment, maintenance, experimentation, and collaboration with product, business development, ML engineering, and IT teams. The role also contributes to data science best practices, internal tooling, peer review, coaching, and translating client analytical needs into commercially viable products.

Advises County departments on responsible enterprise AI adoption, including readiness assessments, use-case prioritization, governance aligned with the NIST AI Risk Management Framework, proof-of-concept oversight, output validation, standards development, stakeholder workshops, training, documentation, and measuring adoption, value, and ROI.

Build and maintain backend data architecture and operational pipelines for a secure geospatial imagery program. Develop, monitor, and sustain data movement across security domains, platforms, systems, formats, and interfaces. Manage cloud-based SQL and NoSQL databases, use APIs for data exchange, and develop pipelines with Python, JavaScript, or Java. Communicate technical architecture to engineering teams and government customers. Preferred experience includes ATO processes, NPE certificate management, SAFe, and pipeline health visualization.