Software Engineer Jobs in San Jose, CA

Responsible for productizing software stack for AI compute engine; focusing on development, optimization, and scaling of AI deployment software, collaborating with hardware and software teams.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

You will port models to new architectures, optimize runtime and communication layers, and develop performance profiling tools for inference operations.

Build critical infrastructure and custom applications, collaborate cross-functionally, and address operational challenges with a strong focus on delivering production-level code rapidly.

Design and build omnichannel advertising systems, integrate team capabilities, mentor engineers, and handle end-to-end project ownership.

Design and build LLM- and RAG-based AI systems to unify multi-source data, enable natural-language queries, automate reporting and metrics, improve localization pipelines, and deliver production-grade APIs and data pipelines to support customer support and analytics.

Design, build, and maintain software solutions to validate, monitor, and operate large GPU clusters for ML researchers. Research and apply AIOps and Agentic AI to reduce operational toil, enable self-service improvements, and participate in on-call support for platform reliability and performance.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Develop and deliver CUDA driver features across pre-silicon simulation, silicon bring-up, and production software. Collaborate with hardware and software teams to design, implement, test, and stabilize GPU compute and graphics functionality across multiple operating systems.

Develop and maintain cloud-native solutions, focusing on CPQ and Order-to-Cash, while collaborating with cross-functional teams to deliver high-quality software solutions.

Lead performance, scalability, and reliability efforts across the platform. Define benchmarks and SLAs; run load, stress, and scalability tests; optimize services, databases, and infrastructure; build observability; lead RCA and long-term improvements; mentor engineers and influence scalable architecture.

The Senior Embedded Security Engineer will enhance the security of the flight software for space vehicles, ensuring high standards for safety and secure coding practices throughout the development lifecycle.

Design, implement, and scale core backend platform services, including distributed systems, event streaming, workflow orchestration, authz/authn frameworks, and high-throughput data platforms. Collaborate with Product, Security, and Infrastructure teams to maintain reliability, security, and scalability standards and enable product teams to build on a resilient backend foundation.

As a Software Engineer, you will design, implement, and optimize software solutions, collaborate with teams, and support various stages of development while shaping company culture.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

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.

Design and build an always-on automation layer for a Rust-based Forecast Engine: scheduled runs, variance/budget tracking, alerting, observability, integrations (Trino, Iceberg, Lightdash, Argo), and the handoff to capacity reservation. Lead architecture, implement reproducible pipelines, testing, and monitoring while collaborating with FinOps, DevOps, and platform teams.

Design and implement scalable, low-latency AI security infrastructure and high-performance networking code in Rust. Collaborate with product and cross-functional teams, debug complex network/system issues, and deliver secure, testable solutions for large-scale cloud security platform.

Develop core backend components for a blockchain network, focusing on networking, reliability, scalability, and high-quality software practices.

Design and develop robotic simulation systems for autonomous driving algorithms and create scalable software for cloud simulation jobs. Collaborate on metrics to evaluate performance.

Lead design and implementation of a scalable chassis automation tool that translates SoC/chassis specifications into generated RTL, register definitions, verification collateral, and integration artifacts. Collaborate with architecture, RTL, verification, and integration teams, enable PPA analysis, integrate frontend/backend flows, drive delivery, and mentor junior engineers while maintaining quality and schedule.

The role involves designing, building, and maintaining AI agents for Roku TV, managing their development lifecycle, and ensuring performance in production environments.

Design and develop automated verification tests for bioinformatics components and NGS pipelines. Work on scalable, distributed sequencing data analysis software, accelerate algorithms on GPUs, and support the software development lifecycle and Design Control process. Collaborate with research and algorithm experts to improve software quality and features for diagnostics.

The role involves designing and developing a physical synthesis core engine, troubleshooting software, and collaborating with R&D teams to support customer needs.

Build scalable, resilient agentic AI and LLM/ML solutions. Translate requirements into high-level and detailed architectures. Implement solutions using Python/C++ and ML frameworks, applying AI/ML fundamentals, data structures, algorithms, and distributed systems.