Machine Learning Jobs in Fremont, CA

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Manage AI engineering teams and client relationships, drive project deliverables, mentor staff, and uphold quality standards while leveraging advanced technologies.

The role involves managing client needs through technology solutions, mentoring team members, analyzing complex problems, and using AI/GenAI to enhance productivity and client relationships.

The role involves designing and implementing AI/ML solutions, leading teams, mentoring staff, and managing project delivery while ensuring client satisfaction.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

As an AI Engineering Manager at PwC, you will lead the design and operation of AI-powered platforms, mentor engineers, and ensure project delivery excellence while focusing on security and scalability.

As a Senior Manager, you will lead finance transformation efforts, assist with Oracle implementations, manage teams, and ensure client satisfaction.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

As a Manager in the Finance team, you will support clients in their Finance Transformation using Oracle Cloud ERP and EPM, providing problem-solving, coaching, and analysis to enhance their financial functions with new technologies.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

In this role, you will lead AI personalization initiatives, develop AI/ML models for user experience, collaborate across teams, and ensure rigorous development practices.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

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 and grow a team of Technical Program Managers who run customer data projects end-to-end: staffing cadence, project setup, throughput and margin tracking, AR/billing hygiene, Airtable as system of record, onboarding TPMs, operational playbooks, and escalation for delivery risk. Serve as a player-coach partnering with Deployment Leads, FDE Manager, and GMs to ensure projects are staffed and delivered reliably.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.

Partner with portfolio and risk managers to develop quantitative risk models, analyze U.S. power markets, improve backtesting and simulation infrastructure, attribute performance, track P&L and exposures, and deliver actionable insights to scale the trading and risk platform.

Lead end-to-end product design for Jerry's mobile and web experiences: map user flows, create wireframes and high-fidelity UI, prototype features, leverage research and analytics, evolve the design system, and apply AI to personalize and simplify car-ownership workflows.

Lead deployment and customer success for autonomous forklift systems: drive onboarding for OEMs, translate field insights into product strategy, scale operations and teams, resolve site escalations, and use data to improve performance and ROI.

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

Lead pre-sales efforts for Media & Entertainment customers: present Snowflake to executives and technical audiences, deliver demos and POCs, design solutions, collaborate with product and engineering, and close large deals while maintaining deep industry and competitive knowledge.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

The Technical Product Manager will manage product lifecycles, coordinate between hardware and software teams, and lead field trials to ensure product success in autonomous vehicle solutions.

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.