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AI Engineer Jobs

Software Engineer, Applied AI, Model Quality

Google

Location
Onsite (New York, NY)
Compensation
$174k - $252k/yr
Employment
Full-time
Level
Senior Level
Posted 5 days ago

About the role

Google is seeking a Software Engineer to join the Google Pics team, an AI-powered visual editor integrated across Google Workspace. The role focuses on building automated evaluation systems for Large Language Models to assess performance metrics like safety and helpfulness, directly impacting the quality of generative AI photo and design capabilities.

Skills

Software Development Software Architecture Machine Learning Model Evaluation Data Processing Debugging Fine Tuning Generative AI Large Language Models Speech/Audio Technology Reinforcement Learning ML Infrastructure Information Retrieval Distributed Computing System Design Natural Language Processing

Benefits

  • Health Insurance

Perks

  • Bonus
  • Equity

Full job details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 5 years of experience with software development in one or more programming languages.
  • 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical field.
  • 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).
  • 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
  • Experience integrating generative AI tools or Large Language Model (LLM) interfaces into workflows.

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.

As a part of Google Pics, an AI-powered visual editor integrated across Google Workspace to bring Generative AI photo and design capabilities directly onto the canvas. In this role, you will work with the image-understanding capabilities of GemPix and Gemini to empower users to edit, transform, and generate images across all Workspace surfaces. You will be executing against a product roadmap, building on limited testing and consumer experiments. You will join a team that requires executing against a roadmap of model capabilities.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

  • Build both human-powered and Large Language Model (LLM)-powered automated evaluation systems to assess model performance.
  • Establish clear metrics to measure aspects like grounding, coherence, safety, and helpfulness.
  • Utilize platforms and tools to efficiently run evaluations across different models and datasets.
  • Provide actionable insights from evaluations to improve model quality, often in collaboration with research, and cross-functional teams.
  • Create tools and systems that make the evaluation process more efficient and effective.