Senior Software Engineer, ML Performance, GenAI, DeepMind
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Minimum qualifications:
- Bachelor's degree in Computer Science, Software Engineering, Mathematics, or a related STEM field or equivalent practical experience.
- 5 years of experience in software development, systems programming, or applied ML engineering or equivalent education credit (e.g., internships, student research, pre-graduate working experience).
- 3 years of experiencewith performance optimization, systems data analysis, visualization tools, or debugging.
- 3 years of experience with Generative AI, Large Language Models (LLMs), Machine Learning and related frameworks.
- 1 year of experience designing and launching production software products or foundation AI systems.
Preferred qualifications:
- Master's or PhD in Computer Science, Artificial Intelligence, Robotics, or related technical field.
- 3 years of experience in a technical leadership role setting architectural direction and guiding project teams.
- Experience developing, evaluating, or serving large language models, agentic workflows, multimodal pipelines, or AI runtime APIs (e.g., PyTorch, JAX, Triton, vLLM, Gemini API).
- Expertise or strong interest in the following areas: performance debugging and optimization of complex workloads, design of performance tools, compiler design and code optimization, high-performance software development techniques, concurrent programming, or multi-core computer architectures.
About the job
At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.
We focus on identifying and achieving performance improvements in production and research ML workloads, with an emphasis on LLM inference—including serving latency, throughput, and cost per token—as well as the emerging demands of agentic workflows on current and next-generation TPU platforms. We work across the entire stack, ranging from model architecture and sharding strategies to Pallas/Mosaic kernels and XLA. Additionally, we partner closely with the Gemini modeling, Deployment, Compiler, and TPU hardware teams to shape the capabilities of next-generation accelerators.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Drive down the cost per token of Gemini inference by optimizing throughput, accelerator utilization, batching, scheduling, and KV cache efficiency. Achieve these gains through expert optimization and by building agentic workflows that enable AI agents to discover, implement, and validate performance improvements at scale.
- Be among the first to bring Gemini to new TPU generations by characterizing hardware at launch, tuning kernels, sharding, and serving configurations for specific memory hierarchies and interconnects, and delivering production-ready performance. Share these insights to influence future TPU hardware and compiler roadmaps.
- Implement model compression techniques—such as quantization and sparsity—to reduce the cost-per-token for production Gemini models. Evaluate new architectures, optimizers, and training methods to achieve target quality with reduced compute requirements.
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