Senior Engineering Manager, Machine Learning

Key Skills

Algorithms, C Programming, C++, Deep Learning, Java Programming, JavaScript, JAX framework, Large scale data processing, Machine learning techniques, Megatron LM framework, Natural Language Processing (NLP), PAX framework, Python Programming, PyTorch, TensorFlow

Job Description

Minimum qualifications:

• Bachelor’s degree, or equivalent practical experience.
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
• 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), or applied Machine Learning (e.g., deep learning, natural language processing).

About the job

Like Google's own ambitions, the work of a Software Engineer (SWE) goes way beyond just Search. SWE Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of engineers. You not only optimize your own code but make sure engineers are able to optimize theirs. As a SWE Manager you manage your project goals, contribute to product strategy and help develop your team. SWE teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

In this role, you will design and develop the libraries and programming environments (e.g., Tensorflow, JAX, PyTorch, Pax, Gemax Prod) that power all Google products, including Ads, Search, YouTube, Waymo, and more. You will be responsible for all Machine Learning in production, with tens of thousands of Machine Learning practitioners across the company and beyond.

The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.

The US base salary range for this full-time position is $231,000-$339,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. 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

• Develop stable, standardized APIs to generically represent and execute Machine Learning programs with an explicit goal to defragment the Machine Learning infrastructure, insulating users from low-level implementation details.
• Accelerate ML innovation by collaborating closely with ML researchers in defining and building LLM frameworks, paving a smooth path from Research-to-Production.
• Support our high ambition, large-model scale-out training.
• Help sustainably position Google's investment across ML frameworks with a focus on both internal and external users.