Machine Learning Jobs in Fremont, CA

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 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.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

The role involves exploring ideas in deep learning, managing machine learning project life cycles, and collaborating on autonomous driving technology.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

The role involves collaborating with customers to solve engineering challenges using machine learning, develop solutions, train teams, and improve products based on feedback.

Research and build efficient ML systems for large-scale LLMs and agentic RL: design algorithms and system techniques, prototype in training/inference stacks, run large-scale experiments, and translate findings into production or publications.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.