Machine Learning Jobs in Irvine, CA

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.

Support the Chief Product Officer to develop product strategy, governance, and roadmap execution. Build planning processes, drive go-to-market strategies, coordinate cross-functional teams, analyze product performance and customer feedback, and integrate product plans with enterprise systems to launch and scale space-related products.

Develop and execute the U.S. Aesthetics AI strategy, identify AI opportunities, support high-priority pilots, monitor progress, and scale successful initiatives. Apply machine learning, generative AI, and agentic AI expertise while establishing governance, enabling technology, ensuring data readiness, and promoting responsible AI. The role requires cross-functional collaboration, operational excellence, and communication across management levels.

Design, deploy, and maintain perception systems for autonomous robots operating in complex real-world environments. Develop 3D perception, LiDAR and vision algorithms, multi-sensor fusion, evaluation pipelines, and safety-critical capabilities. Collaborate with planning and autonomy teams, debug sensor and system integration issues, and optimize algorithms for physical robots under compute and latency constraints. Own perception subsystems end to end, from design and implementation through field validation, regression testing, and long-term maintenance.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

The 12-week MedTech R&D Leadership Development Program internship gives graduate engineering students hands-on project experience, classroom and professional development training, and exposure to R&D platforms. Interns build technical, business, leadership, and cross-functional skills while working on innovative healthcare products and technologies. The program may identify high-performing interns for a full-time leadership program after graduation.

Supports FP&A and enterprise analytics by developing scalable Power BI reporting, financial models, dashboards, and automated processes. Analyzes financial and operational datasets to identify trends, risks, and opportunities; improves forecasting, budgeting, and recurring reporting workflows using Excel, Python, SQL, Power Automate, and Microsoft platforms. Collaborates with finance, data engineering, IT, and business stakeholders, trains users, maintains governance standards, and contributes to the Pigment planning platform. Evaluates AI-enabled tools to improve reporting efficiency, data quality, and decision-making.

An 11-week onsite intern will join either the Data Engineering or Software/Application Engineering track. Responsibilities include developing enterprise applications, front-end interfaces, back-end services, APIs, microservices, databases, data pipelines, and data infrastructure. Interns will integrate data sources, support scientific or business solutions, and apply programming, analytics, machine learning, and generative AI technologies. Most placements are in Lake County, Illinois, with possible assignments at other U.S. locations.

Full-time hybrid summer technology internship at Johnson & Johnson from May 24 through August 13, 2027. Interns contribute to technology projects across software engineering, data engineering, AI and machine learning, cybersecurity, cloud and DevOps, UX, digital health, platform engineering, and connected devices. Responsibilities include building solutions, analyzing data and processes, collaborating with stakeholders, supporting project portfolios, and developing technology capabilities. The program may serve as a feeder into the Technology Leadership Development Program.

Lead Data Scientist partners with business teams to solve problems using data: pull and analyze large datasets, build and deploy statistical and machine learning models, run experiments and causal inference, create dashboards, automate workflows, and mentor junior scientists. Works with data engineers to operationalize scalable AI solutions and communicates insights to non-technical stakeholders to improve efficiency, reduce costs, and enhance member experience.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Leads engineering managers and software teams building customer-facing front-end applications and internal and external APIs. Sets technical direction, oversees delivery, manages risks and dependencies, improves engineering quality and security, develops talent, supports hiring, and partners with product and design. The role drives innovation and scalable software solutions for connected vehicle and fleet operations.

Build and automate data-flywheel systems for autonomous robots, converting large-scale robot experiences into high-quality, versioned training datasets. Responsibilities include data processing, filtering, labeling, auto-labeling, offline inference, hard-example selection, validation, reproducible dataset generation, and incremental training-data pipelines. Partner with researchers and platform teams to productionize experimental workflows and improve the cycle from robot deployment to usable ML data.

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.

Designs and advances the firm's enterprise data and analytics capabilities by building data platforms, warehouses, lakes, semantic models, pipelines, dashboards, and machine learning solutions. Develops AI-ready data assets, predictive models, reporting solutions, and governed analytics capabilities. Provides technical leadership in data architecture, governance, automation, emerging technologies, and analytics strategy while partnering with business and technology stakeholders.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

Lead architecture and implementation of production-grade agentic systems integrating LLMs across applications. Drive LLM integration strategy, cognitive automation roadmap, scalable distributed system design, best practices, cross-functional alignment, and mentor engineering talent.

Lead process assessments and improvement across mortgage operations. Map workflows, identify automation and training gaps, manage vendors, conduct UAT, surface regulatory risks, and present findings to management to streamline operations and improve quality.

As an Enterprise Account Executive at Salesforce, you will drive revenue growth by leveraging AI-driven solutions, understanding client needs, and demonstrating how Agentforce can solve business challenges while fostering client relationships.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

The Director of Robotics Product Safety leads safety vision and execution for autonomous systems, ensuring compliance and risk management, while mentoring the safety engineering team.

Lead enterprise data and analytics strategy for a pharmaceutical company, building cloud-native data platforms, governance, and AI/ML capabilities. Partner with senior leaders, oversee data engineering, integrations (Veeva/Salesforce), vendor relationships, budgets, and a high-performing team to deliver executive insights and self-service analytics while ensuring regulatory compliance.

Lead delivery and maturity of enterprise finance planning, forecasting, reporting, analytics, and data products. Build and coach a high-performing team, develop capability roadmaps, drive adoption of SAP Analytics Cloud and related platforms, and partner across Finance, Technology, and Data to deliver measurable business value through automation, advanced analytics, and scalable solutions.

Lead product strategy and roadmap for recommendation and personalization systems. Partner with ML, behavioral science, design, and engineering to define requirements, run experiments, monitor production performance, and ship recommendation features that improve engagement, retention, and health behavior change.