Machine Learning Jobs in Sunnyvale, CA

The role involves designing and optimizing bidding algorithms for real-time bidding systems, leading projects, and enhancing ad performance through advanced AI techniques.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

The role involves deploying and optimizing LLMs, designing the ML serving stack, and ensuring high-performance GPU services for production readiness.

Lead the development of AI/ML infrastructure for simulations. Collaborate on realism models, scale distributed systems, and ensure alignment with business goals.

As a Machine Learning Engineer Intern, you'll bridge the sim-to-real gap in data for autonomous truck software, optimizing and integrating BEV features and supporting self-play reinforcement learning.

Join PlusAI as a Machine Learning Engineer Intern to develop AI-based features for autonomous trucks, focusing on multi-modal data processing and event classification.

The intern will prototype a generative simulator for diverse driving scenarios, evaluate existing tools, and integrate outputs into data pipelines for autonomous driving applications.

Build and evolve ML infrastructure platform for resource provisioning, workload scheduling, petabyte-scale ETL, feature management, and platform abstractions to support large-scale model development and distributed training.

Build and scale ML infrastructure: automated IaC for GPU/CPU fleets, high-performance workload scheduling, petabyte-scale ETL, feature caching/storage, and platform abstractions to accelerate model development from experiment to production.

Build and improve ML infrastructure and model pipelines for autonomy: optimize models (quantization, pruning, low precision), maintain and profile GPU ML compilers and runtimes, develop a model compiler framework (FTL), and collaborate with autonomy teams to validate and deploy optimized models to fleet vehicles.

Design, build, and deploy ML infrastructure to optimize autonomy models using quantization, pruning, distillation, and model compilers. Maintain and profile GPU ML compilers/runtimes, collaborate with autonomy teams to validate and deploy optimized models to production vehicles.

Design and implement agent harnesses and post-training pipelines, build evaluation benchmarks, develop RL environments and reward models, and debug/optimize multi-GPU training runs to improve LLM capabilities for agentic applications.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

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

Design and implement ML solutions for optimizing GPU accelerated Apache Spark workloads. Collaborate on deployment and lead technical teams in ML.

The Applied Machine Learning Research Scientist will translate ML techniques into scalable systems, improve LLM performance, and optimize workflows while collaborating closely with researchers and engineers.

Lead the evolution of Unity's Ads Experimentation platform as a senior machine learning engineer, focusing on optimizations and statistical methodologies for ad performance.

The role involves developing large-scale data processing pipelines, automating data selection, and collaborating on ML model improvements to enhance vehicle performance and safety.

Contribute to developing machine learning models for multi-scale biology, focusing on design, deployment and optimization across teams and technologies.

Develop ultra-realistic simulations for autonomous vehicles using machine learning, focusing on realism and integration into production systems.

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

Design and deploy next-generation recommendation and ranking models using LLMs, RLHF, and preference learning. Build user-understanding systems, apply RL and optimization to bidding and ad delivery, run causal A/B experiments, and collaborate with engineering to productionize research across the ML pipeline.