Machine Learning Jobs in Santa Ana, CA

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.

Build and deploy production machine learning systems across computer vision, multimodal modeling, structured data, and agentic AI. Own projects from problem definition through monitoring, develop data and evaluation pipelines, establish feedback loops, and translate experiments into practical technical recommendations. Collaborate with product, engineering, operations, annotation teams, and domain experts while delivering maintainable code, documentation, and scalable ML solutions.

Conduct computational research supporting pediatric neuroscience and healthcare improvement. Analyze and integrate fMRI, structural MRI, diffusion MRI, EEG/qEEG, electronic medical record, and other datasets; develop algorithms, simulations, statistical analyses, visual analytics, and predictive models; and collaborate with clinicians and researchers to translate findings into practice.

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.

Leads the bank’s enterprise AI strategy, portfolio, engineering capability, governance, vendor and platform strategy, and workforce adoption. Translates business priorities into production AI initiatives, including generative AI and agentic systems, while ensuring risk controls, security, privacy, auditability, model governance, and measurable value realization. Builds and manages senior AI talent, partners with business and control functions, oversees responsible AI practices, and reports progress, cost, risk, adoption, and outcomes to executive leadership and the Board.

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.

Own product strategy, roadmap, and execution for a high-priority AI-enabled software initiative. Partner with engineering, operations, underwriting, data science, and design to define requirements, guide development, manage tradeoffs, and deliver measurable outcomes. Lead AI evaluation, experimentation, and quality improvement while using data to prioritize investments and measure impact. Communicate strategy and progress to executives and cross-functional stakeholders, coordinating dependencies and resolving blockers.

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.

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.

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 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.

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.

Lead underwriting strategy for AI-enabled products, identify and manage title insurance and model-related risk, advise product/engineering/data teams, ensure regulatory and underwriting compliance, liaison between transaction underwriters and product teams, mentor junior counsel, and contribute to enterprise product underwriting principles.

Lead PRP's Accounts Payable Recovery Audit practice, managing the end-to-end audit lifecycle, P&L performance, client engagements, and a team of managers and analysts. Use analytics, automation, ERP/AP tools, and benchmarking to identify overpayments and recover funds, negotiate with vendors, present findings to finance leaders, and drive process improvements and business development.

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.

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.

Own program-level coordination across AI/ML research and engineering workstreams, tracking commitments, timelines, risks, blockers, and priorities. Translate customer and product requirements into internal plans, support customer deployments, communicate tradeoffs, and drive cross-functional resolution. Build lightweight reporting systems and partner with technical leaders without directing technical decisions. The role requires experience managing AI/ML programs, working with customers, and operating effectively in ambiguous, fast-moving environments.

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