Machine Learning Jobs in Irvine, CA

Leads scientific and technical direction for AI across structural heart applications. Designs, trains, validates, and deploys advanced machine learning models, including foundation, generative, multimodal, clinical language, imaging, and waveform models. Evaluates emerging technologies, establishes scientific standards, mentors technical staff, and partners with engineering, clinical, quality, and regulatory teams on safe, fair, robust, and compliant AI products. Builds data pipelines and MLOps capabilities while supporting model validation, risk management, monitoring, and regulatory documentation.

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.

Develops audio and vision machine learning models and core tools for deployment on edge AI devices and custom hardware accelerators. Responsibilities include prototyping research approaches, optimizing latency, memory bandwidth, data flow, and hardware utilization, collaborating with modeling and embedded software teams, delivering production solutions, and troubleshooting field issues.

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.

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.

Develop and deploy computer vision and machine learning models for semantic and instance segmentation, object and damage detection across 2D and 3D data. Responsibilities include training loops, dataset curation, labeling guidelines, evaluations, ablation experiments, model export, inference code, testing, profiling, and production support. Collaborate with research, backend, operations, and labeling teams while documenting experiments and improving model performance.

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.

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.

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.

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.

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.

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.

Lead development and execution of enterprise AI strategy, governance, architecture, and adoption. Coordinate cross-functional teams to identify use cases, enable responsible AI, support implementations, measure business value, and drive enterprise-wide AI literacy and standards.

Lead GenAI strategy and research to develop scalable LLM and agentic AI applications (e.g., conversational assistants). Analyze data to personalize investment outcomes, collaborate with engineers, productionize models, ensure best practices in data management and evaluation, publish research, and mentor applied scientists.

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.

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.