Machine Learning Jobs in Santa Clara, CA

As a Senior Machine Learning Engineer, you will develop advanced machine learning and deep learning models and platforms for optimizing advertising performance and conduct complex experiments.

Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

The Software Intern at Cadence will work on research and development using machine learning technologies for on-chip parasitic modeling in a team environment.

Drive planning and execution of multiple software and ML workstreams, manage cross-functional dependencies, gather requirements, track schedules, support development-to-deployment, and improve processes to deliver customer product releases.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

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.

Design and implement production-grade VLSI/EDA tools and methodologies for multi-die package design. Build flows, applications, and automation to construct, analyze, validate, and optimize package designs to accelerate tape-out and scale engineering teams.

Administer and continuously improve the OneStream finance platform: design dimensional and cube architecture, manage business rules, integrations, security, release promotion, and production support. Partner with Finance, Accounting, IT, and external consultants to support consolidation, forecasting, reporting, SOX controls, training, and process improvement across development, test, and production environments.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.

Build and manage a machine learning engineering team focused on making engineering drawings and CAD data usable for automation. Lead hiring, onboarding, mentorship, performance, technical direction, design reviews, and production deployment ownership. Guide annotation tooling, active learning, synthetic data augmentation, model health, and organizational growth while remaining technically hands-on and partnering across engineering, technical leadership, and business teams.

Lead strategic positioning and integration of AI-driven and digital platforms for semiconductor process development. Define end-to-end solutions, orchestrate insight integration, partner with product and engineering, identify gaps and high-value AI use cases, and drive adoption and enablement across internal teams and customers.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

Lead a Customer Success team, manage key semiconductor accounts, drive customer adoption, and translate technical challenges into solutions for strategic growth.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

The Data Engineer will gather and process data, analyze trends, support business decisions, and collaborate on predictive models while improving algorithms and efficiency.

The Machine Learning System Software Architect will lead high-performance machine learning software architecture, collaborating with teams to optimize AI solutions, evaluating architecture for performance, and driving innovation in AI training systems.

Seeking a seasoned Photonics Engineer to lead optical integration efforts in semiconductor testing, ensuring signal integrity and system reliability. Responsibilities include design, testing, and overseeing integration in high-volume manufacturing settings, along with mentorship and project management tasks.

Lead full lifecycle of L4 Robotaxi carline programs, build and manage a carline PM team, coordinate software/hardware/vehicle/validation integration, drive ML/data ops and in-car testing, resolve cross-functional issues, and manage customer relationships through productization to mass-production readiness.

Conduct research in machine learning and AI, focusing on robotics and AGI, while collaborating with teams and reporting findings.

Develop ML-first behavior modules for predicting future motions of road users. Collaborate with perception and planning teams to enhance autonomous systems.

Conduct research on AI and robotics, focusing on reinforcement learning, and collaborate on publications and algorithm deployment.

The ML Perception Software Engineer will develop perception modules for autonomous vehicles, focusing on machine learning and computer vision to enhance navigation capabilities.

Conduct research on autonomy and simulation, publish findings, collaborate with teams to deploy algorithms. Focus on scalable autonomy using large-scale data.