Machine Learning Jobs in Irvine, CA

The Head of Engineering will lead AI-first engineering strategies, ensure product quality, build teams, and oversee data-intensive systems for enterprise clients.

As a Machine Learning Engineer II at Capital Group, you will design and implement ML models and data pipelines to enhance investment decisions, collaborate with teams, and ensure quality in your work.

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.

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.

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.

Design, train, and deploy state-of-the-art deep learning models (transformers, CNNs) for end-to-end navigation. Build perception, prediction, and planning solutions, implement data pipelines, deploy and monitor models in production, detect drift, and troubleshoot integration and performance issues for real-world robotic systems.

Design, prototype, and deploy agentic AI solutions and AI Ops infrastructure (orchestration, memory, tooling, observability). Build RAG/retrieval systems, evaluation and testing pipelines, and agent-native workflows to automate engineering and customer-facing tasks. Collaborate with engineering, product, and field teams to scale reliable, field-ready AI for robotics deployments.

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.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.

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

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

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.

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.