Top Machine Learning Engineer Jobs in San Francisco Bay Area, CA

Build and maintain large-scale data pipelines and infrastructure for training data generation, distributed model training, and ML workflows. Support reproducibility, validation, monitoring, and performance optimization. Collaborate with ML engineers and researchers to productionize research and evolve the offline ML platform.

Design, build, and maintain end-to-end ML and data solutions: analyze and preprocess client data, implement ML and deep learning models, deploy LLM/GenAI solutions, build data pipelines, monitor model performance, integrate APIs, collaborate with cross-functional teams, and communicate insights to stakeholders.

Build and improve NLP, retrieval, and LLM systems to extract structured knowledge from messy enterprise data. Work on prompting, fine-tuning, RAG, retrieval stacks (semantic search, vector storage, hybrid approaches), evaluation harnesses, and production pipelines. Collaborate with customers and engineers to define correctness, catch regressions, and ship robust, maintainable ML solutions.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

The Principal Engineer, AI/ML Architecture will define the technical direction for Coupa's AI platform, focusing on model training, evaluation, safety standards, and mentoring a team of ML and data engineers.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

Design, implement, deploy, and maintain LLM-based AI systems for workflow automation, process and entity extraction, dialog/agent functionality, and text generation. Work includes research, model evaluation, production deployment, monitoring, statistical analysis, and collaboration with engineering and product teams using cloud infrastructure (AWS/GCP).

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

The Principal ML Engineer will design and scale ML infrastructure for scientific discovery, collaborating with AI scientists to enhance model training and deployment processes.

The role involves training and developing large-scale image and video models, optimizing data pipelines, and conducting experiments in a high-performance computing environment.

The ML Engineer will architect and build personalization and recommendation systems, design user preference algorithms, collaborate with teams, and improve systems from research to production.

As a Senior Applied ML Engineer at Macroscope, you will design, build, and optimize ML/AI systems, leading the AI Code Review process and ensuring integration with backend systems.

As an intern, you'll develop software and machine learning systems for robots, focusing on perception, intelligence, behavior, and real-time software integration.

The ML Engineer will develop and advance multimodal models for robot perception and behavior, integrating these systems into hardware while collaborating closely with robotics engineers.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Lead an Agile team to design, build, deploy, and maintain production-scale ML models and pipelines. Drive ML architecture, model training, CI/CD, monitoring, and cloud/Kubernetes-based deployments while ensuring responsible, explainable, and well-governed ML solutions.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and maintain scalable ML models and data pipelines to automate logistics processes. Own end-to-end projects from problem definition to production deployment and monitoring. Collaborate with engineering, mentor ML teammates, review code, and evaluate tooling to improve infrastructure and workflows that deliver customer value.

Lead research, development, and deployment of recommendation and personalization ML models. Design algorithms, build end-to-end pipelines, optimize for accuracy/latency/scale, integrate solutions into production, implement monitoring/bias detection/retraining, and collaborate on experimentation and product integration to improve user engagement and retention.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

As an AI/ML Engineer, you'll design, build, and ship large scale NLP and LLM systems for classification, ranking, and content generation while collaborating with cross-functional teams.