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Google Machine Learning Computer Vision Google Jobs

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and graphics ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and graphics ...

Darbytek

Sr. Computer Vision Engineer

Austin, TX · On-site

$180K - $250K/yr

We are seeking a Full-time Sr Level Computer Vision Engineer to help provide expertise to our team ... Design and implement machine learning models that can operate in resource-constrained environments ...

Guru Schools

AI Engineer

Washington, DC · On-site

Expertise deep learning, computer vision, and large language models. * Familiarity with REST APIs ... Google Machine Learning Engineer * SAFe Agile Software Engineer (ASE) * Certification in AI Ethics ...

Mindlance

AI Engineer

Washington, DC

$110.40K - $151.10K/yr

... learning, computer vision, and large language models. * Familiarity with REST APIs, NoSQL, and RDBMS. Certifications (Preferred): * Microsoft Certified: Azure AI Engineer Associate * Google Machine ...

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$25.5K

$42.6K

$88K

How much do google machine learning computer vision google jobs pay per year?

As of Jun 3, 2026, the average yearly pay for google machine learning computer vision google in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is the difference between Google Machine Learning Computer Vision Google vs Data Scientist?

AspectGoogle Machine Learning Computer Vision GoogleData Scientist
Required CredentialsBachelor's or Master's in CS, ML, AI; experience with ML frameworksBachelor's or higher in CS, Statistics, or related field; data analysis skills
Work EnvironmentTech companies, research labs, AI-focused teamsVarious industries including tech, finance, healthcare
Industry UsageDeveloping computer vision models for products/servicesAnalyzing data to inform business decisions
Search & Comparison IntentFocus on ML and computer vision roles at GoogleBroader data analysis roles across industries

Google Machine Learning Computer Vision Google specializes in developing computer vision models using machine learning techniques within Google's ecosystem. Data Scientists analyze data to generate insights across various domains. While both roles require strong analytical skills, Google Machine Learning Computer Vision Google focuses on AI model development, whereas Data Scientists focus on data analysis and interpretation across industries.

Infographic showing various Google Machine Learning Computer Vision Google job openings in the United States as of May 2026, with employment types broken down into 2% As Needed, 17% Full Time, 79% Part Time, and 2% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Senior Software Engineer, Machine Learning, Core ML

Google

Mountain View, CA • On-site

$144.50K - $190.50K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Google rating

8.8

Company rating: 8.8 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

31st of 185 rated software companies

Job description

Minimum qualifications:
  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience programming in Python or C .
  • 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:
  • Master's degree or PhD in Computer Science, Machine Learning, Computer Engineering, or a related technical field.
  • Experience scaling machine learning models (e.g., Large Language Models (LLMs) or foundation models), managing the complexities of transitioning architectures from data-parallel to model, tensor, pipeline-parallel configurations, or related fields.
  • Experience with deep learning frameworks (e.g., JAX, PyTorch, or TensorFlow), including a track record of contributing to or modifying their core internals to support novel and emerging use cases.
  • Experience with co-designing hardware-aware optimizations to accelerate model execution.
  • Knowledge of machine learning compilers (e.g., Accelerated Linear Algebra (XLA) or Multi-Level Intermediate Representation (MLIR)).

About the job
We are the RecML team in Core ML's Applied ML organization. Our mission is to accelerate product innovations through ML for recommendations and user modeling. We deeply engage with Alphabet products areas and partner with them to help accelerate product innovations through applied research in recommendations and user modeling. We generalize successful innovations into standardized, maintainable, and production-grade solutions for use by other teams and products. This opportunity is a horizontal ML infra and efficiency role supporting the training framework of our foundation recommender model and its customers.
The US base salary range for this full-time position is $174,000-$252,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. 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
  • Architect and implement the transition from data-parallel to model-parallel training paradigms.
  • Design and manage large-scale training runs across multi-pod environments, maximizing data center network bandwidth and minimizing communication bottlenecks.
  • Research and integrate transformer model optimizations and novel architectural variants to reduce training time and resource consumption.
  • Write and optimize low-level model code, including custom pallas kernels, to maximize performance out of the hardware.
  • Work cross-functionally with the team and the Kernel optimization team to co-design and implement compiler-level optimizations that accelerate model execution.

Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google's Applicant and Candidate Privacy Policy .
Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .
If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form .
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.

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