Machine Learning Jobs in Santa Clara, CA

Bay Area-based hunter Enterprise Account Executive responsible for prospecting, developing, and closing new enterprise business for Anyscale. Manage complex sales cycles, achieve quotas, lead CxO engagements, qualify opportunities, collaborate with marketing, and drive ongoing account growth while advising customers on ML, compute, and cloud solutions.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

Lead the unified Search and Voice product organization to define multi-year strategy and roadmap for the Roku Entertainment Assistant. Drive prioritization, cross-functional alignment, monetization, AI/ML innovation (LLMs, RAG, conversational AI), experimentation, quality, compliance, and measurable engagement and revenue outcomes while growing and mentoring a high-performing PM team.

Lead technical engagement with MedTech customers to design and deploy AI and accelerated computing solutions. Develop proofs-of-concept, diagnose AI stack performance issues, collaborate with cross-functional teams, document and teach best practices, and build strategic partnerships to drive AI adoption in healthcare.

The Demand Planner is responsible for improving forecast accuracy, analyzing market trends, and collaborating across functions to support supply chain effectiveness and strategic decision-making.

The role involves incorporating machine learning techniques into sensor simulation, working with teams to develop effective models for Lidar, Radar, and Camera systems.

Develop efficient ML solutions for perception including sensor fusion, obstacle detection, scene understanding, and robust onboard systems.

Develop and refine robot behaviors and demonstration software for external events. Write behavior sequencing and control logic, test and debug on hardware, iterate quickly, and support event setup, rehearsals, and live troubleshooting. Collaborate with engineering and operations to deliver polished, reliable demonstrations.

Seeking a Distinguished Technologist to lead AI architecture and design for HPE's Private Cloud AI offerings, focusing on applied AI, agentic AI, and generative AI. Responsibilities include technical leadership, implementation, and mentoring engineers while optimizing AI systems for cloud-based solutions.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

You will own the compilation stack transforming AI models into optimized inference for autonomous driving. This includes developing tools and techniques for model export and optimizing performance while ensuring safety and reliability.

Lead the design and advancement of AI platform infrastructure, focusing on distributed training, efficiency, and optimization of ML models. Collaborate with teams and guide architectural decisions.

Lead design and development of scalable, high-performance ML training infrastructure. Drive distributed training performance optimization, observability, and developer experience. Own cross-functional infrastructure initiatives, set technical direction and standards, and mentor engineers to deliver platform capabilities that support large-scale model training.

The Senior Compiler Engineer will develop a model compilation toolchain for deploying optimized machine learning models in autonomous vehicles, focusing on performance engineering and collaboration with cross-functional teams.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Lead design, build, and operate AIOps and observability platforms (metrics, logs, traces, alerts, dashboards). Define roadmap and standards, collaborate with teams, mentor engineers, and implement ML/LLM-based anomaly detection, forecasting, root-cause analysis, and agentic debugging across large-scale cloud, on-prem, and bare-metal environments.