info_outline XIn most instances, this position requires in-person interviews as part of the hiring process. In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees.

Benefits for this role include: Health, dental, vision, life, disability insurance Retirement Benefits: 401(k) with company match Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Kirkland, WA, USA; New York, NY, USA.

Minimum qualifications: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience. 8 years of experience in software development. 5 years of experience with ML design and 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), or specialization in another ML field. 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

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.

Expertise in statistical modeling or machine learning algorithms. 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.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists.

Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google.

Responsibilities

Provide technical leadership on high-impact projects, managing project priorities, deadlines, and deliverables. Facilitate alignment and clarity across teams on goals, outcomes, and timelines, while influencing and coach a distributed team of engineers.

Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies. Prototype and test fundamental algorithmic approaches, and collaborate with researchers and partners to bring solutions into production environments.