Top AI & Machine Learning Jobs

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Analyze wafer-fabrication, equipment, sensor, metrology, and manufacturing data using statistical analysis, AI, and machine learning. Develop anomaly detection, yield prediction, root-cause, data pipeline, dashboard, and model-monitoring solutions. Integrate diverse manufacturing data sources, support experiments and process improvement, validate and deploy models, and communicate actionable engineering insights to technical and management stakeholders.

Build and operate scalable infrastructure for Zipline’s machine learning lifecycle, including data ingestion, dataset management, training, evaluation, monitoring, and deployment. Improve reproducibility, observability, orchestration, and performance across autonomy-focused ML workflows. The role requires production software engineering in Python, cloud and on-premise infrastructure experience, Kubernetes, ML frameworks, and strong ownership of secure, mission-critical systems.

Supports an AI operations platform by documenting business workflows, testing and QAing AI protocols, triaging bugs, prototyping tools and integrations, maintaining documentation, conducting research, and completing frontend, backend, and API development tasks. The intern collaborates with engineers and business teams while building production solutions using programming languages, databases, APIs, and AI/LLM platforms.

Supports development of an AI feature validation system, including expert-reviewed test sets, scoring logic, automated test execution, regression monitoring, trend dashboards, release gates, and workflow documentation. The intern will conduct structured interviews with nontechnical associates, translate business workflows into test cases, analyze results, and collaborate cross-functionally while learning company processes.

Builds dashboards, applications, automations, ETL integrations, predictive risk models, and AI/ML solutions for unstructured data. Performs data normalization, visualization, business intelligence analysis, and risk assessment improvement. Recommends data storage and integration solutions, creates Power BI documentation, and applies SQL, R, Python, and other data science tools to federal systems and public-sector projects.

Leads firmwide legal AI initiatives by identifying use cases, building and implementing AI agents, evaluating emerging solutions, and supporting attorney adoption. Collaborates with attorneys, IT, vendors, and leadership to diagnose technical issues, manage pilots, document use cases, deliver training and content, assess risks, and provide recommendations aligned with the firm’s AI strategy.

Build and lead the organization-wide adoption of generative AI, agentic systems, and AI-assisted development. Design production automation agents, evaluate coding tools, modernize rules-based workflows with ML, and establish responsible AI guardrails. Partner with engineering, product, data, and clinical teams to identify high-impact opportunities, own the enablement roadmap, measure outcomes, and drive adoption through working software, documentation, and hands-on collaboration.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.

Leads AI/ML transformation and applied research across Global Research. Identifies high-value use cases, configures AI-enabled workflows, develops ML solutions, and embeds AI capabilities into products and instruments. Applies advanced methods including Bayesian optimization, surrogate modeling, generative modeling, and data-driven experimental design. Establishes adoption standards, mentors scientists and engineers, leads cross-functional initiatives, and supports training programs across international research sites.