At a Glance
- Tasks: Lead a team to enhance data quality for generative AI projects.
- Company: Join Google, a leader in tech innovation and digital transformation.
- Benefits: Enjoy competitive pay, remote work options, and a vibrant company culture.
- Why this job: Be at the forefront of AI technology while developing your leadership skills.
- Qualifications: 8 years in software development with Python or C++, plus leadership experience.
- Other info: Work on impactful projects across multiple teams and locations globally.
The predicted salary is between 72000 - 108000 £ per year.
Job Location: Google, London, UK
Advanced Qualifications
- Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.
Minimum Qualifications
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with software development in either the Python or C++ programming languages.
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 3 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
- Experience in Data Quality Engineering, including the design, implementation, and monitoring of data quality processes and systems.
Preferred Qualifications
- Master's degree or PhD in Computer Science, Statistics, Mathematics, or a related technical field.
- Experience in working with Machine Learning (ML)/Generative Artificial Intelligence (GenAI) infrastructure.
- Experience designing and deploying systems and processes to effectively measure, report on, and improve data quality.
- Experience excelling in dynamic, ambiguous environments through exceptional collaboration and communication, including building consensus across teams and articulating complex technical concepts.
- Familiarity with ML production tools and lifecycle.
About the Job
Software Engineering 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 Software Engineering Manager, you manage your project goals, contribute to product strategy, and help develop your team. 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. 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.
Responsibilities
- Lead a team of engineers in alignment with Google's manager expectations by delivering results, building a community, and developing people.
- Drive success in the generative AI space by streamlining quality data collection, and enhance GenAI model training through quantitative pilot studies to identify and implement best practices for human data collection systems.
- Perform productionizing and standardizing methods developed by data scientists for high-quality data and ensure that these metrics are visible to the right stakeholders, meaningful, and actionable.
- Collaborate with horizontal infrastructure teams to monitor and report data quality at every stage of the data collection lifecycle, from collection design through training and model release.
- Contribute to company priorities to improve tooling around ML data needs for GenAI/LLM use cases.
Gen AI Engineering Manager, Human Data Quality employer: Google
Contact Detail:
Google Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Gen AI Engineering Manager, Human Data Quality
✨Tip Number 1
Familiarise yourself with the latest trends in Generative AI and Machine Learning. Understanding the current landscape will not only help you in interviews but also demonstrate your passion for the field, which is crucial for a leadership role.
✨Tip Number 2
Network with professionals in the AI and data quality sectors. Attend relevant meetups or conferences to connect with potential colleagues or mentors who can provide insights into the role and the company culture at Google.
✨Tip Number 3
Prepare to discuss your experience in leading teams and projects. Be ready to share specific examples of how you've influenced stakeholders and solved ambiguous problems, as these are key aspects of the role.
✨Tip Number 4
Brush up on your technical skills in Python or C++. Since the role requires deep expertise in software development, being able to demonstrate your coding abilities during technical discussions will set you apart from other candidates.
We think you need these skills to ace Gen AI Engineering Manager, Human Data Quality
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in software development, particularly with Python or C++. Emphasise your leadership roles and any specific projects related to ML design and data quality engineering.
Craft a Compelling Cover Letter: In your cover letter, explain why you are passionate about generative AI and how your background aligns with the responsibilities of the role. Mention specific experiences that demonstrate your ability to lead teams and manage complex projects.
Showcase Relevant Experience: Detail your experience in data quality processes and systems. Provide examples of how you've optimised ML infrastructure and led teams in dynamic environments, as these are key aspects of the job.
Highlight Collaboration Skills: Since the role requires exceptional collaboration and communication, include examples of how you've built consensus across teams and articulated complex technical concepts in previous positions.
How to prepare for a job interview at Google
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with Python or C++, especially in the context of machine learning. Highlight specific projects where you led ML design and optimised infrastructure, as this will demonstrate your deep expertise in the domain.
✨Demonstrate Leadership Skills
Since the role involves managing a team, be ready to share examples of how you've successfully led projects and influenced stakeholders. Discuss your approach to people management and how you've developed your team members in previous roles.
✨Prepare for Ambiguity
The job description mentions working in dynamic and ambiguous environments. Think of instances where you've navigated uncertainty and how you collaborated with others to find solutions. This will show your adaptability and problem-solving skills.
✨Understand Data Quality Engineering
Familiarise yourself with data quality processes and systems, as this is crucial for the role. Be ready to discuss your experience in designing and implementing these processes, and how you measure and report on data quality effectively.