Top AI & Machine Learning Jobs

Leads Anthropic’s societal harms policy design organization, managing teams focused on child safety, well-being, manipulation, election integrity, and related risks. Sets mitigation strategy across model policies, detection and enforcement systems, and product interventions; coordinates decisions across harm areas; prioritizes resources; handles high-severity escalations; and partners with research, engineering, product, legal, data science, external experts, civil society, and regulators.

Lead technical direction for AI infrastructure, data platforms, shared software primitives, and agentic engineering practices. Build scalable, cloud-native services and secure MCP integrations while establishing AI quality gates, evaluation frameworks, responsible-AI controls, SLOs, and architectural standards. Guide modernization, build-versus-buy decisions, and high-stakes platform choices. Mentor senior engineers and influence technical norms across teams while balancing product delivery, partner needs, member trust, security, and operational quality.

Design, develop, and deploy AI solutions for discriminative and generative applications across semiconductor design, manufacturing, sales, planning, and productivity. Build data pipelines, develop LLM agents, train neural networks, test solutions, and monitor model accuracy and drift. Collaborate cross-functionally to deliver secure, scalable, and high-performance systems.

Design, develop, diagnose, prototype, and deploy machine learning, deep learning, computer vision, and image-processing algorithms for semiconductor inspection equipment. The role involves inventing new algorithmic approaches, training and deploying AI models, creating model architectures, modifying loss functions, and collaborating effectively both independently and within a team.

Analyzes and models used-vehicle residual values and auction prices across markets. Builds statistical models and forecasts, decomposes value gaps, develops dashboards and automation scripts, and translates analytical findings into visualizations and management presentations. Handles descriptive and inferential analysis requests, interprets model results, integrates new data sources and analytical tools, and provides insights supporting Risk, Vehicle Remarketing, Product, and business teams.

Design, build, deploy, and maintain production-grade AI/ML applications (including LLMs and agentic systems). Implement APIs, services, data pipelines, testing, monitoring, and CI/CD. Collaborate with data scientists, platform, security, and product teams to deliver secure, scalable, observable AI solutions that create measurable business value.

Develop and integrate AI/ML capabilities into operational software, including large language models, retrieval-augmented generation, agentic workflows, prompt engineering, and inference pipelines. Evaluate, test, document, and communicate AI system performance, reliability, design decisions, and technical contributions. Preferred experience includes secure development, DevSecOps, Kubernetes, OpenShift, CI/CD, containerized delivery, and classified or regulated environments.

Develops, evaluates, and maintains predictive and statistical models using regression, time-series analysis, and supervised and unsupervised machine learning. Integrates complex aviation, supply chain, and operational datasets; assesses data quality; identifies trends; and documents analytical processes and models. The role collaborates with stakeholders to translate requirements into data science solutions and supports repeatable data, model, and quality-management practices.

Build, test, deploy, and operate production-grade AI solutions, including agent platforms, AI copilots, code-generation tools, evaluation frameworks, and research pipelines. Partner with statisticians and data scientists to improve clinical development workflows, data products, architectures, automation, quality controls, and reporting. Evaluate models and agent designs while optimizing reliability, security, latency, cost, governance, and user outcomes.

Develop, deploy, and maintain scalable ML models (batch and real-time), build end-to-end MLOps pipelines (versioning, CI/CD, containerization, monitoring), analyze large healthcare datasets, bridge research-to-production gaps, present solutions to stakeholders, mentor ML engineers, and apply best practices for model monitoring, drift detection, and retraining.