Senior Staff Software Engineer, Machine Learning, ML Training

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience in software development (e.g., Python).
• 7 years of experience leading technical project strategy, ML design, and

working with ML infrastructure (e.g., model deployment, model evaluation,

data processing, debugging, fine tuning).

• 5 years of experience with one or more of the following: Speech/audio (e.g.,

technology duplicating and responding to the human voice), reinforcement

learning (e.g., sequential decision making), ML infrastructure, or

specialization in another ML field.

• 5 years of experience with design and architecture; and testing/launching

software products.

PREFERRED QUALIFICATIONS:

• Master’s degree or PhD in Engineering, Computer Science, or a related

technical field.

• 8 years of experience with data structures and algorithms.
• 5 years of experience in a technical leadership role leading project teams

and setting technical direction.

• Experience with ML modeling and scaling.
• Experience with large scale distributed systems or machine learning systems

(Training for LLM, image generation).

• Experience with building cloud based services, including with GCP.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change

how billions of users connect, explore, and interact with information and one

another. Our products need to handle information at massive scale, and extend

well beyond web search. We're looking for engineers who bring fresh ideas from

all areas, including information retrieval, distributed computing, large-scale

system design, networking and data storage, security, artificial intelligence,

natural language processing, UI design and mobile; the list goes on and is

growing every day. As a software engineer, you will work on a specific project

critical to Google’s needs with opportunities to switch teams and projects as

you and our fast-paced business grow and evolve. We need our engineers to be

versatile, display leadership qualities and be enthusiastic to take on new

problems across the full-stack as we continue to push technology forward.

With your technical expertise you will manage project priorities, deadlines, and

deliverables. You will design, develop, test, deploy, maintain, and enhance

software solutions.

Google’s revolutionary Cloud TPUs were designed from the ground up to accelerate

machine learning workloads, providing the computational power to train and run

machine learning models in a fraction of the time and cost conventionally

required. With the new Google Cloud Platform (GCP) Cloud TPU service and Google

Kubernetes Engine (GKE) support, it's easy to take your existing ML workloads on

leading ML frameworks (PyTorch and JAX) and move them to the cloud to take

advantage of this great capability.

Google Cloud accelerates every organization’s ability to digitally transform its

business and industry. We deliver enterprise-grade solutions that leverage

Google’s cutting-edge technology, and tools that help developers build more

sustainably. Customers in more than 200 countries and territories turn to Google

Cloud as their trusted partner to enable growth and solve their most critical

business problems.

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

experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google

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

RESPONSIBILITIES:

• Build and deliver ML frameworks to perform training at LLMS and stable

diffusion models for Google Cloud customers.

• Design and implement AI frameworks software for ML workloads in the cloud

that enables models for language modeling, stable diffusion, rankings and

recommendations, etc.

• Identify and resolve issues in software, performance, and topology.
• Work closely with software engineers, product managers and other engineering

teams to get high-quality products and features through the software project

lifecycle.

• Design, develop, test, deploy, maintain, and improve software.