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Google Ai Engineer Jobs (NOW HIRING)

Northramp LLC

AI Engineer (Mid)

Washington, DC · On-site +1

This role sits within an application engineering team and focuses on architecting AI-enabled systems using the Google Agentic Development Kit (ADK), Gemini, and Vertex AI - integrated into enterprise ...

CGI Inc.

AI Engineer

Washington, DC · On-site

AI Engineer Category: Software Development/ Engineering Main location: United States, District of ... Knowledge of cloud platforms for AI deployment (AWS, Azure, or Google Cloud). Familiarity with big ...

Northramp LLC

AI Engineer (Mid)

Washington, DC · Remote

This role sits within an application engineering team and focuses on architecting AI-enabled systems using the Google Agentic Development Kit (ADK), Gemini, and Vertex AI -- integrated into ...

Relanto, Inc.

AI Engineer

Santa Clara, CA · On-site

AI Engineer Duration: 6+ months Location : Bay Area, CA (hybrid) Technology ... Exp in Google Cloud Platform (Gemini, Vertex) Responsibilities: * Design and implement end-to-end ...

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Google Ai Engineer information

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How much do google ai engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for google ai engineer in the United States is $101,752.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $116,500.00 per year, depending on experience, location, and employer.

What are Google AI Engineers?

Google AI Engineers are professionals who design, develop, and implement artificial intelligence and machine learning solutions at Google. They work on a wide range of projects, from improving search algorithms to developing intelligent systems for products like Google Assistant, Photos, and Cloud AI services. Their responsibilities include data analysis, model building, testing, and deployment of AI models in production environments. These engineers often collaborate with researchers, data scientists, and product teams to solve complex problems using the latest advancements in AI and machine learning.

What are some common challenges faced by Google AI Engineers when deploying machine learning models to production?

Google AI Engineers often encounter challenges such as ensuring models are scalable and efficient enough to handle large-scale data, maintaining model performance over time, and addressing issues related to fairness and bias. Collaborating with cross-functional teams, such as product managers and software engineers, is crucial for aligning technical solutions with product goals. Additionally, AI Engineers must keep up with evolving frameworks and best practices to optimize deployment pipelines and monitor models post-launch for potential drift or degradation.

What is the difference between Google Ai Engineer vs Machine Learning Engineer?

AspectGoogle Ai EngineerMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, AI, or related fields; experience with AI frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentTech companies, research labs, AI-focused teamsTech firms, startups, data-driven organizations
Industry UsagePrimarily in AI product development at Google and similar companiesAcross various industries implementing ML solutions
Common Search/ComparisonYesYes

The Google AI Engineer and Machine Learning Engineer roles share many credentials and work environments, but AI Engineers focus more on developing advanced AI models and research, while ML Engineers often implement and optimize machine learning algorithms for practical applications across industries.

What are the key skills and qualifications needed to thrive as a Google AI Engineer, and why are they important?

To thrive as a Google AI Engineer, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree and experience in algorithm development. Familiarity with TensorFlow, Python, cloud computing platforms, and often certifications in AI or data science are essential for daily tasks. Problem-solving abilities, creativity, and effective collaboration are standout soft skills in this role. These skills are vital for developing innovative AI solutions that align with Google’s standards of performance, scalability, and impact.
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Google Ai Engineer jobs near you

Generative AI Engineer (Instructor)

Sizanid Staffing

New York, NY • Remote

Part-time

Posted 21 days ago

Job description

Position: Generative AI Engineer (Instructor)

About Our Client:

Our client, a forward-thinking technology organization, is seeking an experienced Generative AI Engineer to join their team as an Instructor. This role involves not only developing innovative generative AI models and solutions but also training and mentoring teams or clients on the implementation and best practices of generative AI technologies.

Key Responsibilities:
  • Design, develop, and implement generative AI models and applications using state-of-the-art techniques.
  • Deliver training sessions, workshops, and tutorials on generative AI concepts, tools, and best practices to engineers, data scientists, and other stakeholders.
  • Create comprehensive instructional materials, including manuals, slide decks, and hands-on exercises.
  • Stay current with latest research and advancements in generative AI, NLP, and related technologies.
  • Collaborate with cross-functional teams to integrate generative AI solutions into product pipelines.
  • Provide technical guidance and support to teams adopting generative AI technology.
  • Evaluate and optimize existing AI models for performance, scalability, and robustness.
  • Assist in curriculum development for internal training programs related to AI and machine learning.

Requirements

Qualifications & Skills:

  • Advanced degree (Master’s or PhD) in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
  • Bachelors with more 6-8 years experience
  • Proven experience in developing generative AI models using frameworks such as TensorFlow, PyTorch, or similar.
  • Strong knowledge of NLP techniques, transformer architectures (e.g., GPT, BERT), and generative modeling.
  • Experience in instructional design and delivering technical training or workshops.
  • Proficiency in programming languages like Python and relevant AI/ML libraries.
  • Excellent communication and presentation skills, with ability to convey complex technical concepts to diverse audiences.
  • Familiarity with cloud AI platforms (AWS SageMaker, Google AI Platform, Azure ML) is a plus.

Preferred Qualifications:

  • Experience with prompt engineering and fine-tuning large language models.
  • Background in developing AI products or solutions in a commercial environment.
  • Strong teamwork and mentoring abilities.
  • Active contributor to AI research or open-source projects.

Benefits

Part time. Pay depends on experience.