Machine Learning Jobs in Fremont, CA

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 development and execution of a multi-year automation and AI/Industry 4.0 roadmap for manufacturing. Build and mentor a multidisciplinary automation team, oversee end-to-end program delivery (concept through commissioning), integrate ML/vision/IoT/SCADA systems, manage budget and metrics, and drive measurable improvements in safety, quality, throughput, and cost.

The Solutions Engineer will engage with stakeholders to design AI-powered engineering solutions, bridging business needs and technical execution while guiding customers through AI integration.

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 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.

Design, develop, and deploy production machine learning models and pipelines (text, voice, image, tabular). Collaborate with vehicle, energy, and software teams to translate needs into scalable ML solutions, maintain model reliability and alerting, and follow agile practices with clean, modular code.

Seeking Machine Learning Systems Engineers to build end-to-end 3D native ML systems, focusing on training and inference challenges.

The Machine Learning Engineer will design, implement, and optimize reinforcement learning models for autonomous driving systems, enhancing data systems for scalable evaluation and performance assessment.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

The ML Research Scientist will design Bayesian optimization frameworks, collaborate with lab scientists, and integrate ML models with experimental data.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

Develop and train conditioned policies and MARL systems to simulate realistic driving behaviors, implement safety-constrained RL algorithms, design rewards and evaluation metrics, optimize large-scale training pipelines, advance neural architectures for long-horizon planning and spatial reasoning, and integrate research models with production simulation and planning teams.

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

The Senior ML/RL Engineer will develop behavior models, implement RL algorithms focusing on safety, design reward functions and optimize training environments while collaborating across teams.

Design, build, and operate ML infrastructure powering data, compute, artifacts, and orchestration across cloud and on-prem. Own backend services, storage, observability, security, and developer tools; collaborate with cloud/compute providers and lead reliability and scaling efforts.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.