Design and develop virtualisation and container technology for Ubuntu and related open source projects.
Compensation
We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually (and more often for graduates and associates) to ensure we recognise outstanding performance. In addition to base pay, we offer a performance-driven annual bonus.
Responsibilities
Design and specify new features, building consensus on approach
Engage with upstream communities in your area of expertise
Conduct security analysis and reviews to ensure defence in depth
Create and monitor performance benchmarks to optimise your product
Design and build the core research and trading platform, including data pipelines, backtesting and simulation frameworks, portfolio and execution tooling, and research APIs.
Compensation
The annual base salary range for this position is $175,000 to $250,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.
Responsibilities
Design and build the core research and trading platform, including data pipelines, backtesting and simulation frameworks, portfolio and execution tooling, and research APIs
Work closely with quantitative researchers to implement studies, test hypotheses, and translate promising ideas into robust production systems
Develop and productionize statistical and machine learning models, owning the workflow from feature generation and training through backtesting, deployment, and live monitoring
Build reliable data infrastructure for large historical and real-time datasets, with an emphasis on point-in-time correctness, reproducibility, performance, and ease of use
Improve the performance and scalability of computationally intensive research and production workloads
Requirements
Bachelor's degree in computer science, computer engineering, or another technical field
Master's degree in computer science, computer engineering, or another technical field
PhD degree in computer science, computer engineering, or another technical field
Staff Software Engineer, Accelerators Systems Platforms Software Architect
Provide technical leadership on high-impact projects, influence and coach a distributed team of engineers, design, develop, test, deploy, maintain, and enhance large-scale software solutions for Google’s accelerator platforms.
Lead and manage a multidisciplinary engineering team responsible for the reliability, robustness, and pre‑silicon readiness of GSP firmware for NVIDIA GPUs.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 2; and 224,000 USD – 356,500 USD for Level 3.
Responsibilities
Lead, mentor, and develop a multidisciplinary engineering team
Own release execution for GSP firmware across concurrent release trains
Partner with technical leads and architects
Drive measurable improvement in test coverage and stability metrics
Coordinate architectural alignment, project scoping, prioritization, and execution accountability
Requirements
Bachelor's or master's degree in Computer Science, Electrical, or Electronic Engineering
Nice to have: GPU driver development and validation
Nice to have: leading distributed engineering teams across multiple time zones
Senior member of the LLM inference framework team responsible for building and optimizing production-grade single-node and distributed inference runtimes for large language models on AMD GPUs.
Responsibilities
Architect and optimize distributed LLM inference runtimes based on in-house LLM engines or open-source stacks such as vLLM, SGLang, and llm-d
Design and improve TP / PP / EP (MoE) hybrid execution, including KV-cache management, attention dispatch, and token scheduling
Implement and optimize multi-node inference pipelines using RCCL, RDMA, and collective-based execution
Drive throughput, latency, and memory efficiency across single-GPU and multi-GPU clusters
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