Machine Learning Jobs in Santa Clara, CA

Lead engineering teams building analyzers and debuggers (e.g., Intel System Debugger, VTune) to enable observability, profiling, and performance analysis. Define strategy, collaborate across HW/SW, drive technical excellence, mentor engineers, and deliver products for AI/HPC and developer communities.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

As a Customer Success Manager, you will lead initiatives to enhance customer outcomes, manage accounts, and collaborate with engineers to optimize our SaaS platform.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

The Developer Relations Manager will drive the adoption of NVIDIA's AI platforms in research labs, leveraging expertise in physics and AI while collaborating with various teams to enhance research outcomes.

The Senior Developer Relations Manager will engage with developer ecosystems in agriscience, driving the adoption of NVIDIA technologies through technical guidance, collaboration, and strategic advocacy. Responsibilities include advising on resources, onboarding partners, monitoring ecosystems, and collaborating across functions to promote successful integrations.

As a Staff AI Research Engineer, you will develop AI solutions, implement machine learning algorithms, benchmark techniques, and transition prototypes to production.

Design and implement real-time lane-fusion algorithms that combine multi-source perception and temporal data to produce stable 3D lane and road representations. Research solutions for occlusion, fragmentation, forks/merges, and construction zones. Build a 3D laneline pipeline with camera perception, optimize for low latency and high availability on safety-critical vehicle platforms, and collaborate with cross-functional teams to deploy on target systems.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

As a Senior AI/ML Engineer, you'll enhance developer productivity by leading CI solutions, mentoring engineers, and implementing scalable systems.

Analyze and reduce RTL and pre‑silicon power for GPUs and networking chips using EDA tools and AI/ML. Run workloads, perform comparative power analysis, prototype Verilog features, debug power anomalies, and collaborate with architects, RTL designers, and physical design teams to drive power improvements.

Lead design and implementation of agentic workflows, optimization algorithms, and ML/DL solutions for EDA. Research novel ML approaches, develop and maintain software (APIs, C/C++, Python) on Linux, and collaborate with global cross-functional teams. Requires ML/DL framework experience (PyTorch) and LLM/agentic AI understanding.

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Lead the development of AI applications, ensuring high reliability and performance while collaborating with teams to identify use cases and implement scalable solutions. Drive AI methodologies, governance, and continuous improvement in the organization.

Lead end-to-end data science within R&D: extract and analyze clinical and operational data, build and validate advanced ML/AI and LLM-integrated solutions, translate insights for stakeholders, mentor teams, and drive product innovation for Invisalign.

The Software Engineer, Perception develops autonomous aircraft systems to detect and manage wildfires using real-time data processing and machine learning techniques.

Own and deliver Auditoria's AP/P2P product: set strategy and OKRs, manage roadmap and prioritization, ship enterprise SaaS AI features, enable sales/partners, support pre/post-sales, and collaborate cross-functionally to meet revenue and customer success goals.

Develop and automate software tests and CI/CD pipelines for robotic surgical systems; define and execute test plans, analyze test data, log and debug issues, produce dashboards and reports, and contribute to reliability and verification strategy for electromechanical embedded platforms.

Develop and execute automated software tests and test protocols for a robotic surgical platform; contribute to CI/CD pipelines, test plans, reliability verification, data analysis, issue logging, cross-functional debugging, and generate dashboards and executive summaries.

Develop real-time intraoperative ML and computer vision software for robotic-assisted minimally invasive surgery. Design, prototype, implement, test, and deploy algorithms and user-facing components, support system integration, produce documentation and patents, and iterate from clinical feedback to deliver production-ready code.

Develop and implement large-scale machine learning and foundation-model systems, collaborate with research teams to integrate technologies, maintain ML lifecycle (data preprocessing, pre-/post-training, evaluation), conduct design reviews and code reviews, debug system issues, contribute to documentation and research publications, and represent the institute at conferences.

Design and ship deterministic, safety-critical classical or ML motion planners for fallback and minimal-risk maneuvers. Build planners for degraded perception and partial observability, evaluate behavior at scale using simulation and real-world data, develop metrics and analysis tools, and collaborate with perception, prediction, controls, safety, and remote assistance teams to deliver a reusable fallback platform.

Design, implement, and evaluate perception algorithms and 4D world representations for autonomous vehicles. Train and deploy ML and computer vision models from data collection to production, test on real vehicles, and collaborate with data, behavior, and research teams to deliver production-ready autonomy software.

Design and implement low-speed motion planning and control modules for autonomous vehicles, characterize vehicle dynamics to close sim-to-real gaps, evaluate academic research, and build optimal-control solutions (e.g., nonlinear MPC, MPPI) deployable on production vehicle platforms.