Top AI & Machine Learning Jobs

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.

Lead Clover-wide AI product vision and roadmap, scaling generative-AI and predictive solutions for merchant commerce. Drive cross-functional delivery, AI governance, MLOps enablement, commercialization, and people leadership to launch and optimize AI-enabled POS and SaaS offerings.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.