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Staff Software Engineer, ML Infrastructure, Core Infra

Google

Staff Software Engineer, ML Infrastructure, Core Infra

Google

·

On-site

·

Full-time

·

2w ago

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.

Our team is focused on bringing Google's Applied AI to the market! Our mission is to pioneer the development of conversational AI, enabling developers to build generative AI agents capable of engaging in complex, open-ended dialogues and completing autonomous tasks. Gemini said
Core Infra provides the foundation for this mission, driving critical efforts like Centralized Compliance, Conversation History Logging powering various customer facing and internal analytics. You will be passionate about building centralized expertise for horizontals or compliance and advance road map for Applied AI (AAI) products in this space.

Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Deep Mind/Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.

The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

  • Drive the technical direction and multi-year road map for conversational agent data systems, collaborating with Principal Engineers to align infrastructure capabilities with the rapid evolution of Generative AI and Agentis verticals.

  • Architect and implement scalable, high-performance data solutions using Spanner and Google Cloud Platform (GCP), balancing the demands of real-time runtime storage with the complexity of massive-scale offline analytics.

  • Act as the primary technical liaison for cross-functional partner teams, clarifying project scope and setting the architectural direction for both the producers and consumers of core product data.

  • Advocate a culture of production excellence by leading design and code reviews across cross-site organizations.

  • Support critical infrastructure investments that address scalability bottlenecks, technical debt, and operations, guiding the team through complex migrations in a high-velocity environment.

Minimum qualifications

  • Bachelor's degree or equivalent practical experience.

  • 8 years of experience in software development.

  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

  • 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 ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

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.

  • 3 years of experience in a technical leadership role leading project teams and setting technical direction.

  • 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.

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About Google

Google

Google

Public

Google specializes in internet-related services and products, including search, advertising, and software.

10,001+

Employees

Mountain View

Headquarters

$1,700B

Valuation

Reviews

3.7

25 reviews

Work-life balance

3.8

Compensation

4.2

Culture

3.4

Career

3.9

Management

2.8

68%

Recommend to a friend

Pros

Excellent compensation and benefits

Smart and talented colleagues

Great perks and work flexibility

Cons

Management and leadership issues

Bureaucracy and slow processes

Constantly changing priorities and reorganizations

Salary Ranges

57,502 data points

Junior/L3

L3

L4

L5

L6

L7

L8

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Junior/L3 · Data Scientist L3

0 reports

$176,704

total per year

Base

-

Stock

-

Bonus

-

$150,298

$203,110

Interview experience

9 interviews

Difficulty

3.4

/ 5

Duration

14-28 weeks

Offer rate

44%

Experience

Positive 0%

Neutral 56%

Negative 44%

Interview process

1

Application Review

2

Online Assessment/Technical Screen

3

Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

Common questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Product Sense