Senior Software Engineer, ML Performance, GenAI, DeepMind

About the role

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.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

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.