Machine Learning Jobs in San Jose, CA

Design and implement efficient algorithms to optimize LLM inference on DNN accelerators. Collaborate with mathematicians, ML researchers, and engineers to apply advanced algorithmic and numerical techniques for high-impact generative AI performance improvements.

Lead software engineer responsible for designing high-level and detailed architectures, performance tuning, and building distributed systems. Work with LLM/ML and big-data technologies (e.g., Langchain, Spark), develop in Python and C++, and design data solutions with strong SQL and database fundamentals. Collaborate across teams and integrate diverse frameworks into practical solutions.

Design and develop Bird’s Eye View fusion models for autonomous driving using multimodal sensor inputs like LiDAR, focusing on performance, robustness, and scalability.

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.

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.

This role involves full-cycle recruiting for technical talent in fast-paced environments, building partnerships, sourcing from unconventional channels, and improving recruiting systems.

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.

Apply machine learning to real-time graphics and interactive experiences: develop models from latest research, prototype applications, collaborate across teams, and productize neural graphics features for gaming and visualization.

Design, build, and operate production ML systems and LLM-based agents for fraud and risk detection at a global crypto exchange. Responsibilities span feature engineering, model training and serving, monitoring, explainability, integration with operations, and building safe, auditable LLM agents for investigation and automation.

Design, build, and deploy scalable agentic AI systems for semiconductor design verification. Implement agent infrastructure, retrieval and data pipelines, evaluation frameworks, CI/observability, and optimize for latency, cost, reliability, and scalability in production EDA environments.

The role involves developing privacy technology in machine learning, collaborating on modeling, and creating solutions for TikTok's product privacy requirements.

Design and build machine learning algorithms to optimize e-commerce search and improve overall platform matching capabilities, supporting TikTok's rapid e-commerce development.

Develop and optimize Python and C++ software to compress, convert, deploy, and run neural networks on analog compute-in-memory hardware. Implement quantization, pruning, graph optimizations, runtime APIs, and profiling tools. Collaborate with researchers and hardware engineers, perform testing, debugging, and CI, and support research, documentation, and validation of hardware-software co-design for edge and embedded AI systems.

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.

Develop and deploy machine learning algorithms for planning in autonomous trucks, ensuring safety and real-time efficiency.

Own and optimize the AI inference and execution layer: quantization, KV-cache, batching, latency/memory tuning, fine-tune and evaluate models, build bounded task runtimes, enable hardware acceleration and partner with systems/backend teams to ship production-grade agentic AI capabilities.

Develop algorithms and optimizations for inference and compiler stack, enhance performance metrics, collaborate with hardware teams, and publish work on novel approaches.

Define and execute the scientific roadmap for search and recommendation: architect large-scale ranking and retrieval systems, develop and distill Ranking Foundation Models, integrate vector/RAG/hybrid retrieval into production, partner with product and engineering, and lead and scale multidisciplinary ML teams to optimize marketplace health.

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

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 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.

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 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.