Machine Learning Jobs in Irvine, CA

Designs and deploys scalable AI and machine learning systems using Python and TensorFlow. Responsibilities include data wrangling, complex analysis, NLP, deep learning, data pipeline management, data integration, and model optimization. The role also involves stakeholder collaboration, aligning AI initiatives with business goals, mentoring junior team members, and contributing to data infrastructure and engineering practices.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads Oracle Cloud Finance transformation workstreams, client engagements, and delivery teams across ERP and EPM programs. Designs finance processes, translates business requirements into Oracle Financials solutions, oversees configuration and deployment planning, and applies automation, analytics, and machine learning to improve finance operations. Manages risks, timelines, deliverables, and stakeholder relationships while coaching team members and identifying opportunities to improve budgeting, forecasting, reporting, and financial close processes.

Leads financial statement audits and evaluates governance, risk management, regulatory compliance, and internal controls for digital asset and financial services clients. Directs complex audit engagements, applies advanced auditing and technical accounting expertise, identifies material misstatements, and issues auditor reports. The role also involves client relationship management, coaching and developing diverse teams, communicating findings, and maintaining professional, independence, and ethical standards.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Own the vision and strategy for Toast’s computer vision products across enterprise drive-thru operations. Lead zero-to-one discovery, prototyping, pilots, commercialization, and scaling of AI and hardware-enabled solutions. Define roadmaps, evaluate technology and vendor strategies, conduct customer research, and align Engineering, Design, Hardware, Sales, Legal, and other teams. Drive product adoption, customer value, and enterprise growth while balancing technical performance, scalability, cost, and operational complexity.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Develop and scale machine learning recommendation systems for Xumo streaming products. Responsibilities include feature engineering, big-data analysis, model training and deployment, offline evaluation, online A/B testing, data pipeline management, model serving optimization, technical documentation, proofs of concept, and cross-functional collaboration. The role also mentors junior engineers and contributes to patents and APIs.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Analyzes clinical trial enrollment, costs, resourcing, and clinical intelligence to support forecasting and strategic decisions. Develops reports, dashboards, and analytical tools using clinical models, leads portions of clinical projects, collaborates with cross-functional stakeholders and vendors, and recommends process improvements within Clinical Affairs for medical device therapies.

The Enterprise Account Executive will drive strategic technology sales from opportunity creation through closure, focusing on Microsoft business applications and Azure solutions for manufacturing clients. Responsibilities include developing account and territory plans, managing sales cycles, collaborating with Microsoft field teams and internal specialists, building C-level relationships, supporting marketing and lead generation, and accurately forecasting sales performance.

Provides statistical support for pharmaceutical research and development, focusing on biomarker studies, precision medicine, clinical trials, pharmacology, and multi-dimensional data analysis. Designs studies, performs statistical analyses, identifies patient subgroups and disease markers, evaluates predictive models, ensures data quality, and communicates scientifically sound findings to cross-functional research teams. Supports biomarker analysis plans, publications, and decision-making across drug development programs under supervision.

Two-year early-career rotational program with three cross-functional assignments focused on data engineering and analytics. Participants create analytical reports, work with machine learning and artificial intelligence, lead intensive business projects, and develop technical, interpersonal, and leadership skills. The program provides exposure to the full data lifecycle, business strategy, and continuous improvement, with potential placement into a post-program role.

Develops, tests, and optimizes signal-processing and physiological measurement algorithms for medical devices. Analyzes clinical and physiological datasets, performs modeling and experimentation, and supports algorithm development from feasibility through validation and product integration. Collaborates with software, hardware, systems, clinical, and quality engineering teams, and contributes to clinical data collection and human-subject study analysis.

Leads the architecture, development, optimization, and validation of ECG signal-processing algorithms for cardiac monitoring. Responsibilities include waveform detection, feature extraction, rhythm and arrhythmia classification, real-time embedded implementation, clinical dataset validation, and translating clinical and regulatory requirements into compliant algorithm designs. The role provides technical and project leadership while collaborating with hardware, software, systems, and clinical teams through commercialization.

Develops advanced machine learning, deep learning, signal processing, optimization, and numerical modeling algorithms for biomedical signals and next-generation medical devices. Responsibilities include algorithm design, implementation, testing, documentation, data analysis, disease detection and prediction modeling, patentable innovation, and collaboration with hardware and software engineering teams.

Own failure triage and reliability improvements for deployed autonomous robots. Analyze robot behavior, logs, telemetry, and model outputs; build datasets and evaluation sets; train and fine-tune ML models; and partner with modeling, data, deployment, and robotics teams to validate and ship improvements across real-world sites.

An 11-week onsite internship offering Data Engineering or Software/Application Engineering tracks. Interns may build enterprise applications, APIs, microservices, user interfaces, databases, data pipelines, and data infrastructure while supporting analytics and scientific or business solutions. Candidates must be pursuing a relevant bachelor’s degree, remain enrolled after the internship, and graduate between December 2027 and June 2028. Preferred qualifications include programming experience, AI or machine learning exposure, teamwork, communication, and project management skills.

Leads the end-to-end design, deployment, and performance of real-time ML prediction and bid-optimization systems for programmatic advertising. Provides hands-on technical leadership to applied scientists and ML engineers, formulates optimization problems, manages production ML challenges such as latency, delayed feedback, drift, and retraining, and aligns technical roadmaps with business priorities.

Provides statistical leadership for pharmaceutical research, clinical trials, patient safety, medical affairs, regulatory submissions, and product lifecycle management. Develops statistical, machine learning, and AI methodologies; designs protocols and analysis plans; conducts complex analyses; supports benefit-risk assessment and real-world evidence studies; ensures data and reporting quality; and leads cross-functional collaboration and strategic innovation.

Build, deploy, and maintain machine learning systems, including data pipelines, feature engineering workflows, model training, evaluation, APIs, microservices, and batch or streaming components. Monitor model performance, data drift, anomalies, and retraining triggers. Collaborate with engineering, data science, product, and business teams while following governance, documentation, coding, and source control standards.

Develop and deploy predictive and generative AI models for personalization, forecasting, marketing automation, conversational agents, and other business applications. Perform exploratory analysis, statistical modeling, causal inference, and A/B testing. Build Python-based modeling workflows, collaborate on productionization and MLOps, develop RAG and agentic systems, and communicate insights to technical and non-technical stakeholders across the organization.