Job Description
Company Overview
Google is a global leader in technology and innovation, dedicated to organizing the world’s information and making it universally accessible and useful. With a strong focus on artificial intelligence, Google’s products and services—from Search and Ads to Cloud and YouTube—rely on cutting‑edge machine learning to deliver personalized experiences at scale. The company’s research labs and engineering teams collaborate across borders to solve complex problems, foster responsible AI, and push the boundaries of what is possible.
Key Responsibilities
Key Responsibilities For Machine Learning Engineer
- Design, develop, and deploy scalable machine learning models that address product‑specific challenges in areas such as recommendation systems, natural language understanding, and computer vision.
- Collaborate with data scientists, product managers, and software engineers to translate research prototypes into production‑ready pipelines and services.
- Conduct rigorous experiments, perform model validation, and implement A/B testing frameworks to measure impact on key performance metrics.
- Optimize algorithms for performance, latency, and cost efficiency on Google Cloud Platform and on‑device environments.
- Maintain high standards of code quality, documentation, and testing using modern software engineering best practices.
- Contribute to the AI community by publishing findings, presenting at internal forums, and mentoring junior team members.
Required Qualifications
- Bachelor’s or higher degree in Computer Science, Electrical Engineering, Statistics, or a related quantitative field; advanced degree preferred.
- Minimum 3 years of professional experience building and shipping machine learning solutions in a production environment.
- Proficiency in Python and at least one deep‑learning framework such as TensorFlow or PyTorch.
- Strong foundation in algorithms, data structures, and software engineering principles.
- Demonstrated ability to work with large‑scale data sets using SQL, BigQuery, or similar technologies.
- Excellent problem‑solving skills and the ability to communicate complex technical concepts to diverse audiences.
Desired Skills
- Experience with distributed training, model serving (TensorFlow Serving, KFServing), and containerization (Docker, Kubernetes).
- Knowledge of reinforcement learning, generative models, or multimodal AI.
- Familiarity with MLOps tools such as MLflow, TFX, or Kubeflow.
- Contributions to open‑source projects or published research in top AI conferences.
- Understanding of privacy‑preserving machine learning and responsible AI practices.
Compensation & Benefits
- Base salary range: $150,000 – $210,000 annually, commensurate with experience and location.
- Annual performance bonus and equity awards.
- Comprehensive health, dental, and vision coverage.
- Generous parental leave, paid time off, and flexible work arrangements.
- Professional development stipend, tuition reimbursement, and access to internal learning resources.
- On‑site amenities such as fitness centers, meals, and wellness programs at the Mountain View campus.
How to Apply
Visit the Google Careers portal, search for “Machine Learning Engineer,” and submit your resume, a cover letter highlighting relevant projects, and any supporting portfolio or publications. The application process includes an online assessment, technical interviews focused on coding and ML system design, and a final interview with the hiring team. Candidates selected will be contacted to schedule the interview series.
Google is a global leader in technology and innovation, dedicated to organizing the world’s information and making it universally accessible and useful. With a strong focus on artificial intelligence, Google’s products and services—from Search and Ads to Cloud and YouTube—rely on cutting‑edge machine learning to deliver personalized experiences at scale. The company’s research labs and engineering teams collaborate across borders to solve complex problems, foster responsible AI, and push the boundaries of what is possible.
Key Responsibilities
Key Responsibilities For Machine Learning Engineer
- Design, develop, and deploy scalable machine learning models that address product‑specific challenges in areas such as recommendation systems, natural language understanding, and computer vision.
- Collaborate with data scientists, product managers, and software engineers to translate research prototypes into production‑ready pipelines and services.
- Conduct rigorous experiments, perform model validation, and implement A/B testing frameworks to measure impact on key performance metrics.
- Optimize algorithms for performance, latency, and cost efficiency on Google Cloud Platform and on‑device environments.
- Maintain high standards of code quality, documentation, and testing using modern software engineering best practices.
- Contribute to the AI community by publishing findings, presenting at internal forums, and mentoring junior team members.
Required Qualifications
- Bachelor’s or higher degree in Computer Science, Electrical Engineering, Statistics, or a related quantitative field; advanced degree preferred.
- Minimum 3 years of professional experience building and shipping machine learning solutions in a production environment.
- Proficiency in Python and at least one deep‑learning framework such as TensorFlow or PyTorch.
- Strong foundation in algorithms, data structures, and software engineering principles.
- Demonstrated ability to work with large‑scale data sets using SQL, BigQuery, or similar technologies.
- Excellent problem‑solving skills and the ability to communicate complex technical concepts to diverse audiences.
Desired Skills
- Experience with distributed training, model serving (TensorFlow Serving, KFServing), and containerization (Docker, Kubernetes).
- Knowledge of reinforcement learning, generative models, or multimodal AI.
- Familiarity with MLOps tools such as MLflow, TFX, or Kubeflow.
- Contributions to open‑source projects or published research in top AI conferences.
- Understanding of privacy‑preserving machine learning and responsible AI practices.
Compensation & Benefits
- Base salary range: $150,000 – $210,000 annually, commensurate with experience and location.
- Annual performance bonus and equity awards.
- Comprehensive health, dental, and vision coverage.
- Generous parental leave, paid time off, and flexible work arrangements.
- Professional development stipend, tuition reimbursement, and access to internal learning resources.
- On‑site amenities such as fitness centers, meals, and wellness programs at the Mountain View campus.
How to Apply
Visit the Google Careers portal, search for “Machine Learning Engineer,” and submit your resume, a cover letter highlighting relevant projects, and any supporting portfolio or publications. The application process includes an online assessment, technical interviews focused on coding and ML system design, and a final interview with the hiring team. Candidates selected will be contacted to schedule the interview series.
Required Skills
- Machine
- Learning
- Engineer