Machine Learning Jobs in Irvine, CA

The Sr Engineer will develop AI/ML models for health monitoring devices, focusing on algorithm development, data analysis, and cross-functional collaboration.

Drive the full enterprise sales cycle for Microsoft-based solutions in manufacturing. Develop account and territory plans, co-sell with Microsoft, engage C-level stakeholders, manage forecasts, and collaborate with internal specialists to close opportunities.

Summer data science internship working on ML projects (anomaly detection, numerical optimization, PII/PHI NLP). Interns will develop models, write high-quality Python, use Linux and development tools, work with git, Docker, CI/CD and Kubernetes, participate in code reviews and agile rituals, and present results to technical and non-technical audiences.

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Design and build distributed ML infrastructures to optimize data processing, model training, and evaluation among teams.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

As a Senior Machine Learning Platform Engineer, you will manage ML infrastructure, optimize pipelines, mentor junior engineers, and ensure observability and compliance in production environments.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Design, develop, and deploy scalable machine learning models and data pipelines for production. Collaborate with data scientists, engineers, and stakeholders, optimize model performance and scalability, automate deployments, monitor production systems, and evaluate emerging ML and cloud technologies to drive business outcomes.

Deploy, maintain, and improve production ML workflows and platforms for media measurement and customer analytics on AWS. Build SageMaker jobs and endpoints, orchestrate pipelines with Step Functions, Lambda, and Airflow, manage model artifacts and CI/CD, troubleshoot production issues, and collaborate on platform enhancements and operational best practices.

As Principal Product Manager, you will drive strategy and execution for Viant DSP's supply products, conducting research, managing product development, and collaborating with teams to address customer needs.

Support design, training, evaluation, deployment, and monitoring of ML models for document classification, entity extraction, and outcome prediction. Help integrate AI tools into legacy workflows, track metrics (precision/recall/accuracy), assist model versioning and logging, and collaborate across teams while gaining hands-on experience with tax-focused AI systems.

Lead AI/ML product strategy and execution: translate business needs into scalable, secure AI-enabled solutions; define product vision and governance; prioritize and manage backlog; partner with engineering, data science, and platform teams; ensure technical soundness, MLOps readiness, and regulatory/responsible AI alignment; drive delivery, adoption, and measurable business value from ideation through launch and post-launch evaluation.

As an AI/ML Engineer, you will develop multimodal AI models, focusing on computer vision and agentic capabilities. Responsibilities include data curation, model training, evaluation, and deployment while working with real robotic systems.

The AI/Machine Learning Engineer II will design and develop models to analyze data for health monitoring devices, focusing on diagnosis and prediction using various algorithms.

Design, research, and implement Generative AI models to enhance investment processes. Collaborate with teams to deliver enterprise-grade solutions and lead projects while solving complex machine learning problems.