Machine Learning Jobs in San Jose, CA

This role involves improving AI and ML infrastructure for researchers by identifying bottlenecks, optimizing performance, and collaborating with diverse teams.

Design, build, and operate large-scale ML systems for ad performance: feature engineering, model training/deployment (batch and real-time), evaluation, monitoring, and production optimization while collaborating with cross-functional teams.

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

As an intern, you will research and implement algorithms in computer vision and machine learning, developing new features for Sony's digital imaging technology.

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.

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.

The AI/ML Engineer designs and operates a production-grade orchestration platform, focusing on multi-agent workflows, LLM integration, and backend systems. Responsibilities include managing cloud-native infrastructure, ensuring system reliability and observability.

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 operate ML infrastructure for LLMs and agent systems: model gateways, routing, serving, telemetry, observability, evaluation, cost-aware routing, dashboards, and APIs/SDKs to enable reliable production AI at scale.

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.

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

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

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

Engineer will leverage AI for pre-silicon verification, collaborate with teams, develop AI solutions, and engage customers regarding verification strategies.

The Principal Compiler Engineer will lead compiler engineering, optimizing ML model performance and coordinating with development teams to innovate compiler infrastructure.

The ML Features Solutions Engineer will develop, optimize, and deploy core machine learning features for enterprise applications while collaborating with cross-functional teams to ensure production-grade quality and performance.

As a Senior ML Infrastructure Engineer, you will design scalable architectures for ML, build robust data pipelines, manage GPU clusters, and drive improvements in CI/CD workflows while collaborating with cross-functional teams.

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 a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.